dataesr/scientific-paragraphs-categorization huggingface dataset:
Comparison with Anders et al. (2019) Anders et al. (  2019 ) derived stellar parameters, distances, and reddenings for 285 × 10 6 stars down to G = 18 mag, using the StarHorse code  (Queiroz et al. 2018 ) and combining photometric catalogues and parallaxes from Gaia DR2. We calculated the difference between the StarHorse stellar parameters and the ones we either assumed or derived. We applied four filters to the StarHorse dataset: the first one with all the sources in common, the second one with a clean sub-sample that fulfils SH_GAIAFLAG="000" and SH_OUTFLAG="00000" (see  Anders et al. 2019  for an explanation), the third one only shows the members after our work, and the fourth one is the clean sub-sample of the previous one. We provide the results grouped into stellar associations in Table  E.
Acknowledgements. A special thanks to  Alcione Mora  for his help with the Gaia data,  Floor van Leeuwen  for his help with the size of the clusters,  Ana María Álvarez García ,  María Teresa Galindo Guil  and  Irene Pintos-Castro  for their comments and suggestions,  Agnes Monod-Gayraud  for her careful reading of the manuscript and English corrections, and the anonymous referee for the comments that helped to improve the quality of this manuscript. FJGG acknowledges support from  Johannes Andersen Student Programme at the Nordic Optical Telescope. This research has been funded by the  Spanish State Research Agency (AEI)  Project No. PID2019-107061GB-C61  and No. MDM-2017-0737  Unidad de Excelencia "María de Maeztu" -Centro de Astrobiología (INTA-CSIC ); AB acknowledges support by  ANID , - Millennium Science Initiative Program -NCN19_171  and  BASAL  project  FB210003 , and from  FONDECYT Regular 1190748 ; VOSA, developed under the  Spanish Virtual Observatory  project supported from the  Spanish MINECO  through grant  AyA2017-84089 ; the astronomical java software TOPCAT (Taylor 2005) and STILTS (Taylor 2006);  NASA 's Astrophysics Data System Bibliographic Services; ESASky, developed by the  ESAC Science Data Centre (ESDC)  team and maintained alongside other  ESA  science mission's archives at  ESA 's  European Space Astronomy Centre (ESAC, Madrid, Spain) ; the  SVO Filter Profile Service  (http://svo2.
cab. inta-csic. es/theory/fps/)supported from the  Spanish MINECO  through grant  AyA2014-55216 ; data from the  European Space Agency (ESA)  mis-sion Gaia (https://www. cosmos. esa. int/gaia),processed by the Gaia Data Processing and Analysis Consortium (DPAC, https://www. cosmos. esa. int/web/gaia/dpac/consortium). Funding for the DPAC has been provided by national institutions, in particular the institutions participating in the Gaia Multilateral Agreement; The Pan-STARRS1 Surveys ( PS1 ) and the PS1 public science archive have been made possible through contributions by the  Institute for Astronomy , the  University of Hawaii , the Pan- STARRS  Project Office, the  Max-Planck Society  and its participating institutes, the  Max Planck Institute for Astronomy, Heidelberg  and the  Max Planck Institute for Extraterrestrial Physics, Garching , The  Johns Hopkins University, Durham University , the  University of Edinburgh , the  Queen's University Belfast , the  Harvard-Smithsonian Center for Astrophysics , the  Las Cumbres Observatory Global Telescope Network Incorporated , the  National Central University of Taiwan , the  Space Telescope Science Institute , the  National Aeronautics and Space Administration  under Grant No.
NNX08AR22G  issued through the  Planetary Science Division of the NASA Science Mission Directorate , the  National Science Foundation  Grant No. AST-1238877 , the  University of Maryland, Eotvos Lorand University (ELTE) , the  Los Alamos National Laboratory , and the  Gordon and Betty Moore Foundation ; the SIMBAD database, operated at  CDS, Strasbourg, France ; Funding for the  Sloan Digital Sky Survey IV  has been provided by the  Alfred P. Sloan Foundation , the  U. S. Department of Energy Office of Science , and the Participating Institutions. SDSS acknowledges support and resources from the  Center for High-Performance Computing at the University of Utah. The SDSS web site is www. sdss. org. SDSS is managed by the  Astrophysical Research Consortium  for the Participating Institutions of the SDSS Collaboration including the  Brazilian Participation Group , the  Carnegie Institution for Science, Carnegie Mellon University , the  Chilean Participation Group , the  French Participation Group, Harvard-Smithsonian Center for Astrophysics ,  Instituto de Astrofísica de Canarias , The  Johns Hopkins University, Kavli Institute for the Physics and Mathematics of the Universe (IPMU)/University of Tokyo , the  Korean Participation Group ,  Lawrence Berkeley National Laboratory, Leibniz Institut für Astrophysik Potsdam (AIP), Max-Planck-Institut für Astronomie (MPIA Heidelberg), Max-Planck-Institut für Astrophysik (MPA Garching), Max-Planck-Institut für Extraterrestrische Physik (MPE), National Astronomical Observatories of China, New Mexico State University ,  New York University, University of Notre Dame, Observatário Nacional/MCTI , The  Ohio State University, Pennsylvania State University ,  Shanghai Astronomical Observatory, United Kingdom Participation Group ,  Universidad Nacional Autónoma de México ,  University of Arizona, University of Colorado Boulder, University of Oxford, University of Portsmouth, University of Utah, University of Virginia, University of Washington, University of Wisconsin, Vanderbilt University, and Yale University ; data products from the  Wide-field Infrared Survey Explorer , which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory/California Institute of Technology , funded by the  National Aeronautics and Space Administration ; data products from the  Two Micron All Sky Survey , which is a joint project of the  University of Massachusetts  and the  Infrared Processing and Analysis Center/California Institute of Technology , funded by the  National Aeronautics and Space Administration  and the  National Science Foundation.
2022, 2200166 2200166 (4 of 23) NCs (≈200 μL), washed with the ethanol and deionized water two times, and then dry-dispersed on a Cu-lacey carbon grid. The RT, heat-, and light-tolerated PL spectra were collected using a homebuilt PL data collection system operated with a 405 nm CW laser. The simplified sketch of this PL setup is depicted in Figure  1b. It consists of a halogen lamp with output power spanning from 0 to 100 W, a shamrock 303i spectrometer, a 460 nm long-pass filter (i. e. , used for filtering the collected PL signals), a Nikon Ti-U Inverted Microscope (which can be used for bright-field reflection), and a bright-field transmission. The heating-cooling PL measurements were in situ recorded on a UK THMS600 Linkam device coupled to the Nikon Ti-U inverted Microsope, with a heating-cooling rate of 150 °C min -1 over the temperature range of -150 ↔ +150 °C, a focused laser spot (here the objective of a Nikon 50× TU Plan APO was used) on a fixed surface of the samples, and a fixed slit (e.
(5 of 23) reached 3 h, for example, the spectrometer was then turned on and started to measure the PL spectra. For the laser cut-off process, the samples were continued to be kept in the sample chamber. Once the cut-off time come to a given time like 10 h, the laser was turned off and then the spectrometer started to measure the PL spectra. The measurement time used for collecting each PL enhancement and depreciation data was comparatively short, just 0. 01 s. Additionally, the RT PL spectra of the samples upon excitation with a 375 nm CW laser were also collected, but the PL data collection system, as depicted in Figure  1c , differed in that of Figure  1b. This setup used a single microscope objective (NA = 0. 8) for the PL collection. The collected PL signals were transmitted through a 405 nm long-pass filter and fiber-coupled to an optical spectrometer (SR-303I-A spectrograph with DU-420-0E CCD).
Structural and Microstructural Analysis of Samples Representative X-ray diffraction (XRD) patterns of GIG-CsPbBr 3 and GIG-CsPbBr 3 :0. 5%Ln 3+ (Ln = La, Lu) samples are shown in Figure  2a. They confirm that the major 2-reflection peaks at the planes (110), (  200 ), (211), and (220) agree with that of Inorganic Crystal Structure Database (ICSD) file no. 29073. Referring to those earlier works dedicated to CsPbBr 3 -related glass nano-vitroceramics  [12, 25, 33, 34,  and relevant XRD data that we recompiled into Table  2 , it seems that the cubic phase is preferred for CsPbBr 3 crystallized in glass matrix. Nevert eless, we cannot fully exclude the form of an orthorhombic distortion in the perovskite lattice and the possible existence of a very small percentage of orthorhombic phase-typed CsPbBr 3 NCs based upon our experimental XRD patterns, so that these issues still remain open in the GIG-MHPs-typed NCs vitroceramics at this stage.
Acknowledgements F. K. and Y. D. contributed equally to this work. In this work, F. K. thanks for the financial support of the  European Union 's  Seventh framework programme  ( FP7) and Horizon 2020 research and innovation program  ( H2020 , No. 609405 ) under the  Marie Sklodowska-Curie grant  (grant no. 713683 ) (COFUNDfellowsDTU), the  "Spring Buds Talent" Program  and the starting grant from  Ningbo Institute of Materials Technology and Engineering (NIMTE) Chinese Academy of Sciences (CAS) , and the " Double Hundred Talent " project and the starting grant from  Sichuan University. Y. D. thanks the financial support of the  National Science Foundation of China for Youth  (Grant No. 11804160 ). M. W. acknowledges the support by the  Danish National Research Foundation through NanoPhoton -Center for Nanophotonics , Grant No.
DNRF147  and  Center for Nanostructured Graphene , Grant No. DNRF103 , and by the  Independent Research Fund Denmark -Natural Sciences  (Project No. 0135-00403B ). K. Z. thanks the financial support from the  Independent Research Fund Denmark-Sapere Aude  starting grant (Project No. 7026-00037A ), and  Swedish Research Council   VR starting grant  (Project No. 201705337 ). The TEM measurements were supported by  DTU Nanolab , the  National Centre for Nano Fabrication and Characterization at the Technical University of Denmark (DTU). All the authors are also very grateful to the anonymous reviewers who raised useful and helpful comments to this work.
Acknowledgements The authors would like to thank  Francesco Benini ,  Davide Cassani ,  Oscar Henriksson ,  Kiril Hristov ,  Hyojoong Kim ,  Silviu Pufu ,  Alessandro Tomasiello ,  Thomas Van Riet  for useful discussions and correspondence. We thank  Tarek Anous  and  Iosif Bena  for useful comments on the draft of this paper. Finally, we are indebted to  Frederik Denef  for numerous discussions throughout the completion of this project, for elucidating some subtleties regarding the probe action and for giving feedback. CT acknowledges support from the  NWO Physics/ f grant n. 680-91-005. RM was supported by  ERC   Starting Grant 679278 Emergent-BH  and  NSF  grant  PHY- 19-14412.
In agreement with previous experiments, the FG-repeat Figure  5. Single-cell force spectroscopy shows that both the F-and FG-repeats adheres strongly to fibrillated fibronectin (Fn). Single Staphylococcus carnosus cells expressing either F-or FG-repeats were immobilized on a colloidal atomic force microscopy (AFM) cantilever, and force-distance curves were measured by approaching and retracting the cantilever to surfaces with fibrillated or globular Fn. Adhesion events are recognized as negative peaks on the force axis below the horizontal baseline. The online version of this article includes the following source data for figure  5 : Source data 1. Zip file containing raw force spectroscopy data and screenshots to document system setup. did not interact with the globular form of Fn (Figure  6 ).
The operation parameters were set to optimize resolution with minimum possible damage or artifact from contaminations on the tip. Typically, scans were started with a large scan area of minimum 10 × 10 µm² with a rather low scan resolution of 64 × 64 pixels and a high scan rate >1 Hz. Once an area of interest was identified, a higher resolution image 256 × 256 or 512 × 512 pixels of a smaller scan are was acquired at lower scan speeds (<1 Hz). The acquired data was processed using Gwyddion open software (http://gwyddion. net/) for necessary corrections of tilt, etc.
Analyzer pass energy of 160 eV was used for compositional survey scans of C 1s , O 1s , N 1s , Na 1s , Si 2p , Cl 2p, P 2p , and K 2p. High-resolution scans of C 1s and N 1s elements were collected at an analyzer pass energy of 20 eV. Compositions and fits of the high-resolution scans were produced in CasaXPS. The data is presented in a table as an average and standard deviation of the three sample spots. FTIR analysis of Fn adsorbed to PEA and PMA FTIR measurements were performed on a Bruker Vertex v70 with 128 scans per spectrum and a 7 mm diameter beam spot. The concentration of Fn (20 µg ml -1 ) results in very small IR absorbances of the polymer layers, therefore, the spectra of stacks of eight coated CaF2 windows were measured simultaneously. For this, eight spin-coated CaF2 window surfaces with PMA and PEA were incubated for 1 hr with the 20 µg ml -1 Fn solution in PBS prepared in D 2 O (d-PBS hereafter), after rinsing the surfaces with d-PBS and placed four sets of windows (spaced by 25 µm Teflon spacers that were filled by d-PBS, with the polymer and protein-coated sides submerged in the d-PBS) in a custom-made IR cell.
These effects nearly cancel each other. In addition, the growth rate is nearly the same everywhere  (Ballantyne et al. , 2012). We therefore maintain an uncertainty around the annual growth rate based on the multiple stations dataset ranges between 0. 11 and 0. 72 GtC yr -1 , with a mean of 0. 61 GtC yr -1 for 1959-1979 and 0. 17 GtC yr -1 for 1980-2020, when a larger set of stations were available as provided by  Dlugokencky and Tans (2022). We estimate the uncertainty of the decadal averaged growth rate after 1980 at 0. 02 GtC yr -1 based on the calibration and the annual growth rate uncertainty but stretched over a 10-year interval. For years prior to 1980, we estimate the decadal averaged uncertainty to be 0. 07 GtC yr -1 based on a factor proportional to the annual uncertainty prior and after 1980 (0.
Instead, for land, they become the net uptake of CO 2 by vegetation and soils that is not exported by fluvial systems, similar to the DGVM estimates. For oceans, they become the net uptake of anthropogenic CO 2 , similar to the GOBM estimates. The inversion systems prescribe global fossil fuel emissions based on the GCP's Gridded Fossil Emissions Dataset versions 2022. 1 or 2022. 2 (GCP-GridFED;  Jones et al. , 2022) , which are updates to GCP-GridFEDv2021 presented by  Jones et al. (2021). GCP-GridFEDv2022 scales gridded estimates of CO 2 emissions from EDGARv4. 3. 2  (Janssens-Maenhout et al. , 2019)  within national territories to match national emissions estimates provided by the GCB for the years 1959-2021, which were compiled following the methodology described in Sect. 2. 1. Small differences between the systems due to, for instance, regridding to the transport model resolution or use of different GridFED versions with different cement carbonation sinks (which were only present starting with GridFEDv2022.
, 2022 , LDEO-HPD, Bennington et al. , 2022; Gloege et al. , 2022). These are based on a multi-linear regression of pCO 2 with environmental predictors  (Rödenbeck et al. , 2022, included here)  or on model-data pCO 2 misfits and their relation to environmental predictors  (Bennington et al. , 2022). The Jena-MLS estimate falls well within the range of GOBM estimates and has a correlation of 0. 98 with S OCEAN  (1959-2021 and 1959-1989). It agrees well on the mean S OCEAN estimate since 1977 with a slightly higher amplitude of variability (Fig. 10 ). Until 1976, Jena-MLS is 0. 2-0. 3 GtC yr -1 below the central S OCEAN estimate. The agreement, especially on phasing of variability, is impressive, and the discrepancies in the mean flux 1959-1976 could be explained by an overestimated trend of Jena-MLS  (Rödenbeck et al.
B2 ) shows a root-mean-squared error (RMSE) from annually detrended data of 0. 4 to 2. 6 µatm for the seven f CO 2 -based data products over the globe, relative to the f CO 2 observations from the SOCAT v2022 data set for the period 1990-2021. The GOBM RMSEs are larger and range from 3. 0 to 4. 8 µatm. The RMSEs are generally larger at high latitudes compared to the tropics, for both the data products and the GOBMs. The data products have RMSEs of 0. 4 to 3. 2 µatm in the tropics, 0. 8 to 2. 8 µatm in the northern extratropics (> 30 • N), and 0. 8 to 3. 6 µatm in the southern extratropics (< 30 • S). Note that the data products are based on the SOCAT v2022 database; hence, the SOCAT is not an independent data set for the evaluation of the data products. The GOBM RM-SEs are more spread across regions, ranging from 2.
Global sinks and spread of estimates In the period 2012-2021, the bottom-up view of total global carbon sinks provided by the GCB, S OCEAN for the ocean and S LAND -E LUC for the land (to be comparable to inversions), agrees closely with the top-down global carbon sinks delivered by the atmospheric inversions. Figure  12  shows both total sink estimates of the last decade split by ocean and land (including E LUC ), which match the difference between G ATM and E FOS to within 0. 01-0. 12 GtC yr -1 for inverse systems, and to 0. 34 GtC yr -1 for the GCB mean. The latter represents the B IM discussed in Sect. 3. 8, which by design is minimal for the inverse systems. The distributions based on the individual models and data products reveal substantial spread but converge near the decadal means quoted in Tables  5  and 6.
, 2001; Lacroix et al. , 2020)  applied to f CO 2 -based data products and inverse systems to isolate the anthropogenic S OCEAN flux. Other possibly contributing factors are that the data products potentially underestimate the winter CO 2 outgassing south of the Polar Front  (Bushinsky et al. , 2019)  and potential model biases. CO 2 fluxes from this region are more sparsely sampled by all methods, especially in wintertime (Fig. B1 ). Dominant biases in Earth system models are related to mode water formation, stratification, and the chemical buffer capacity  (Terhaar et al. , 2021; Bourgeois et al. , 2022; Terhaar et al. , 2022). The interannual variability in the southern extratropics is low because of the dominance of ocean areas with low variability compared to land areas. The split between land (S LAND -E LUC ) and ocean (S OCEAN ) shows a substantial contribution to variability in the south coming from the land, with no consistency between the DGVMs and the inversions or among inversions.
Discussion Each year when the global carbon budget is published, each flux component is updated for all previous years to consider corrections that are the result of further scrutiny and verification of the underlying data in the primary input data sets. Annual estimates may be updated with improvements in data quality and timeliness (e. g. to eliminate the need for extrapolation of forcing data such as land use). Of all terms in the global budget, only the fossil CO 2 emissions and the growth rate in atmospheric CO 2 concentrations are based primarily on empirical inputs supporting annual estimates in this carbon budget. The carbon budget imbalance, while an imperfect measure, provides a strong indication of the limitations in observations in understanding and representing processes in models and/or in the integration of the carbon budget components.
The persistent unexplained variability in the carbon budget imbalance limits our ability to verify reported emissions (Peters et al. , 2017) and suggests we do not yet have a complete understanding of the underlying carbon cycle dynamics on annual to decadal timescales. Resolving most of this unexplained variability should be possible through different and complementary approaches. First, as intended with our annual updates, the imbalance as an error term is reduced by improvements of individual components of the global carbon budget that follow from improving the underlying data and statistics and by improving the models through the resolution of some of the key uncertainties detailed in Table  10. Second, additional clues to the origin and processes responsible for the variability in the budget imbalance could be obtained through a closer scrutiny of carbon variability in light of other Earth system data (e.
Conclusions The estimation of global CO 2 emissions and sinks is a major effort by the carbon cycle research community that requires a careful compilation and synthesis of measurements, statistical estimates, and model results. The delivery of an annual carbon budget serves two purposes. First, there is a large demand for up-to-date information on the state of the anthropogenic perturbation of the climate system and its underpinning causes. A broad stakeholder community relies on the data sets associated with the annual carbon budget including scientists, policy makers, businesses, journalists, and nongovernmental organizations engaged in adapting to and mitigating human-driven climate change. Second, over the last decades we have seen unprecedented changes in the human and biophysical environments (e.
Data availability The data presented here are made available in the belief that their wide dissemination will lead to greater understanding and new scientific insights of how the carbon cycle works, how humans are altering it, and how we can mitigate the resulting human-driven climate change. Full contact details and information on how to cite the data shown here are given at the top of each page in the accompanying database and summarized in Table  2. The accompanying database includes three Excel files organized into the following spreadsheets. The file Global_Carbon_Budget_2022v0. 3. consumption country CO 2 emissions from fossil fuels and cement production and emissions transfer from the international trade of goods and services (1990-2020) using CDIAC/UNFCCC data as reference; 4. emissions transfers (consumption minus territorial emissions; 1990-2020); 5.
country definitions. The file National_LandUseChange_Carbon_Emissions _2022v0. 1xlsx includes the following items: 1. summary 2. territorial country CO 2 emissions from land-use change (1850-2021) from three bookkeeping models; All three spreadsheets are published by the Integrated Carbon Observation System (ICOS) Carbon Portal and are available at https://doi. org/10. 18160/GCP-2022 (Friedlingstein et al. , 2022b). National emissions data are also available from the Global Carbon Atlas (http://www. globalcarbonatlas. org/, last access: 25 September 2022) and from Our World in Data (https://ourworldindata. org/co2-emissions, last access: 25 September 2022).
SOCATv2022 f CO 2 data were converted to dis-solved inorganic carbon (DIC) with the TA. Fields of DIC were estimated by using a MLR method based on the DIC and satellite observation data. Geospatial random clus-ter ensemble regression is a two-step cluster-regression approach, where multiple clustering instances with slight vari-ations are run to create an ensemble of estimates. We use K-means cluster-ing and a combination of gradient-boosted trees and feed-forward neural networks to estimate SOCAT v2022 f CO 2. Based on f CO 2 misfit between observed f CO 2 and eight of the ocean bio-geochemical models used in this assessment. The extreme gradient boost-ing method links this mis-fit to environmental ob-servations to reconstruct the model misfit across all space and time, which is then added back to model-based f CO 2 esti-mate.