dataesr/scientific-paragraphs-categorization huggingface dataset:
| Carboxy-methylcellulose overlay concentration Overlay is added onto the cell monolayer to limit the virus diffusion within the plate which permits plaque formation. The concentration of the methylcellulose overlay medium is critical to ensure data reliability. YF 17D strain titration was conducted as previously described using three different concentrations (0. 6%, and 1. 6%) of CMC overlay to determine the final concentration which suited well for PS cells and avoided the spreading of the virus on the entire plate.
ACKNOWLEDGMENTS We are grateful to  Dr Antoine Diatta  and  Yellow Fever Quality Control team of Institut Pasteur de Dakar. We also thank the  staff of Virology Department of Institut Pasteur de Dakar  involved in the different activities described in this work particularly  Djibril Ndione ,  Idrissa Dieng ,  Ibrahima Bamba Ndione  and  Pape Ibrahima Diop  for their contributions. The laboratory work was funded by  Institut Pasteur de Dakar. This research received no external funding.
g. ,  Kulkarni 2012; Pietka et al. 2015; Villar et al. 2017  for optical wavelengths,  Eftekhari et al. 2022  for millimeter wavelengths, or  Metzger et al. 2015  for radio wavelengths), which is facilitated by the significant volume of available data. We build on the first attempts to produce an observationdriven DLPS in the X-rays  (Soderberg et al. 2009; O'Brien & Smartt 2013 ) by populating the DLPS with light curves as a comprehensive view of the low-to intermediate-redshift (z ≤ 1) phase space for (observer frame) 0. 3-10 keV transient and variable X-ray phenomena. This extends the use of the DLPS by showing both luminosity and time evolution of these events. The motivation behind compiling this dataset is two-pronged: to identify pristine regions of this parameter space that can be explored by future observing facilties (i.
For some classes the data are sparse, and we will instead plot peak X-ray luminosity (L x ) vs. duration. In the one case where there were insufficient (confirmed ) observations, we used theory as a supplement. We present in Section 2 the datasets for each of the different classes of transient and variable events, and we discuss their location within the DLPS. In Section 3, we examine the use cases for our comprehensive DLPS (Figures  1  and 2 ). Where available, redshifts were used to correct the duration to the rest frame as well as to determine the luminosity distance, assuming a cosmology with H 0 = 69. 6 km s -1 Mpc -1 , Ω M = 0. 286, and Ω Λ = 0. 714.
Supernovae Supernova (SN) shocks that propagate in the explosion's environment are well-known particle accelerators and well-known sources of X-ray emission as the shocks decelerate and the particles cool down (e. g. , Chevalier & Fransson 2017 for a recent review). We collected Xray data for supernovae from a variety of sources (see Appendix A for details). Because of the rather limited sample of existing observations, we include all available (z ≤ 1) X-ray light curves in bands with lower energy limits between 0. 2 and 0. 5 keV and upper energy limits between 8 and 12 keV, which are then k-corrected to the observed 0. 3-10 keV band assuming a spectrum F ν ∝ ν -β with a spectral index β = 1 (equivalent to a photon index Γ = 2). These k-corrected data are shown in Figure  3. We divide the SNe into three sub-classes based on their underlying physical properties: Type I corecollapse to be comprised of Type Ib, Ic, Ib/c, Ic/pec, and IIb SNe; Type II core-collapse to be comprised of Type II, IIP, IIL, and IIpec SNe; and Interacting SNe (i.
Software: AstroPy  (Astropy Collaboration et al. 2013 , 2018) , Matplotlib  (Hunter 2007) , NumPy  (Harris et al. 2020) , pandas (pandas development team 2020; Wes McKinney 2010), PIMMS  (Mukai 1993) , scikit-learn  (Pedregosa et al. 2011) , SciPy  (Virtanen et al. 2020)  Sensitivity limit, 1ks (erg cm 2 s 1 ) FOV = 1 deg 2 Figure 8. To decouple the advantages of increased field of view and sensitivity, we show extragalactic transient rates  (Stone & Metzger 2016; Coppejans et al. 2020; Margutti & Chornock 2021; Ghirlanda & Salvaterra 2022)  as a function of FOV with a fixed flux limit (left, sensitivity = 10 -13 erg s -1 cm -2 ) and the same rates as a function of sensitivity with a fixed field of view (right, FOV = 1 deg 2 ). As in the rest of this work, we limit the rates to z ≤ 1 -which is why the number of observations per year eventually flattens with increasing sensitivity.
As with other variable phenomena that show recurrent outbursts and flares, some objects may correspond to a number of unique light curves. a These data are not shared in the GitHub repository. b The light curves and quiescent behavior of Aql X-1 and GX 339-4 are shown in the X-ray phase space. c We note that CI Cam is a ULX as long as it is at a distance > 8 kpc.
ACKNOWLEDGMENTS The authors thank  Laura Chomiuk ,  Irina Zhuravleva , and  Margaretha Pretorius  for helpful discussions. K. L. P. acknowledges support from the  UK Space Agency. This work made use of data supplied by the  UK Swift Science Data Centre  at the  University of Leicester. R. M. acknowledge partial support by the  National Science Foundation  under Grant No. AST-2221789  and  AST-2224255 , by the  Heising-Simons Foundation  under grant #  2021-3248. This research has made use of MAXI data provided by  RIKEN ,  JAXA  and the  MAXI  team.
Material and gas phase properties The heat capacity, the melting temperature and the heat of melting of LPDE were obtained from differential scanning calorimetry. The evolution of the LDPE density with temperature was taken from literature  [32]  and it has been checked that the literature values at room temperature are equal to those measured in our experiments. The thermal conductivity of LPDE was measured using the transient line source method. These data are given in Table  2  for the LDPE and NiCr. The gas-phase density, heat capacity and conductivity are evaluated by assuming that the gas is air and by using a temperature evaluated as the average between the adiabatic flame temperature of ethylene in the investigated conditions and the ambient temperature.
This study investigates the role of servant leadership in creating a positive school climate. A quantitative research design was used to examine the relationship between servant leadership and school climate, including the effects of servant leadership on student academic achievement, teacher job satisfaction, and school culture. The study was conducted in several schools in a particular district, and data were collected using a survey questionnaire from 40 teachers and 1000 students. Data were analyzed using descriptive and inferential statistics. The findings suggest that there is a significant positive correlation between servant leadership and teacher job satisfaction (r = 0. 55, p < 0. 01), school culture (r = 0. 62, p < 0. 01), and academic achievement (r = 0. 45, p < 0. 01). The results indicate that servant leadership practices contribute to the development of a positive school climate.
Data Collection Data for this study were collected using a survey questionnaire. The questionnaire was adapted from previous studies that measured servant leadership, school climate, and academic achievement. The questionnaire consisted of four sections: demographic information, servant leadership, school climate, and academic achievement. The servant leadership section of the questionnaire consisted of 20 items adapted from the Servant Leadership Questionnaire (SLQ) developed by  Barbuto and Wheeler (2006). The items were measured using a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). The school climate section of the questionnaire consisted of 15 items adapted from the School Climate Survey (SCS) developed by  Kohl et al. (2013). The items were measured using a Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree).
RESULTS The data collected from the survey questionnaire were analyzed to examine the relationships between servant leadership, school climate, and academic achievement. The results are presented in tables below. Table  2  presents the descriptive statistics for servant leadership, school climate, and academic achievement. The mean score for servant leadership was 3. 87 for teachers and 3. 63 for students. The mean score for school climate was 3. 92 for teachers and 3. 78 for students. The mean score for academic achievement was 3. 73 for students. Table  3  presents the correlation analysis for servant leadership, school climate, and academic achievement. The results show a significant positive correlation between servant leadership and school climate (r = 0. 78, p < 0. 001), indicating that schools with higher levels of servant leadership also have more positive school climates.
There, one needs to consider the discrete nature of quantum spins via, e. g. , exact diagonalization (ED). Since ED for S = 5/2 is particularly costly in computer time and memory, we will restrict calculations to a single tetrahedral unit of five spins, fitted to high-temperature experimental data. Such an approximation shall provide an independent estimate of the energy scales, to be compared with parameters obtained from DFT and Monte Carlo simulations. In the rest of this Letter, S i will denote a three-component classical spin of length |S i | = 5/2, except when analyzing specific-heat data where S i is a quantum S = 5/2 spin. Comparison between experiment and theory. To explore the magnetism of [Mn(ta) 2 ], we measured magnetic susceptibility over three decades in temperature. Fitting the high-temperature regime (T > 200 K) of the susceptibility χ with a Curie-Weiss law [Fig.
Acknowledgments. We are grateful to  Nic Shannon  for stimulating discussions. D. V. and R. R. -O. are grateful for financial support from  DFG  Grant No. VO-829/12-2  ( DFG Priority Program 1928  " Coordination Networks: Building Blocks for Functional Systems ") and SEM micrographs taken by  Ralph Freund. A. A. T. , A. J. , P. G. , and H. -A. K. v. N. were supported by the  German Research Foundation  via  TRR80. L. D. C. J. acknowledges financial support from the  "Agence Nationale de la Recherche " under Grant No. ANR-18-CE30-0011-01. R. P. N. , L. P. , and L. D. C. J. acknowledge financial support from the  LMU-Bordeaux Research Cooperation Program. R. P. N. and L. P. acknowledge support from  FP7/ERC  Consolidator Grant QSIMCORR, No. 771891 , and the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany's Excellence Strategy -EXC-2111 -390814868.
Nevertheless, the findings from these results are subject to various uncertainties stemming from factors such as acquisition method, image resolution, frequency of image acquisition, the meteorological context, image interpretation, and diagnostic bias  [48] [49] [50] [51]. The question of uncertainty in damage evaluation can be addressed by comparing this assessment with other estimations. With this in mind, the ANR-TIREX project  [52]  has compared assessments using two approaches: a satellite-based approach (Copernicus (https://emergency. copernicus. eu/, accessed on 10 July 2023) and SERTIT (https://plateforme. icube. unistra. fr/sertit/index. php/Accueil, accessed on 10 July 2023)) and a flying drone approach  [53] , which normally gives a better assessment of building damage. This comparison can be further complemented by the assessments carried out by M.
Data needs and preparation, and user choices Five decision variables are required for the decision tree framework to be applied to a species or group of species. The first decision variable informs on the current population functionality in the catchment. Here, we chose the definitions of Barber-O'Malley et al. (2022b) which evaluate the sustainability of a population on two broad criteria. The first one estimates the number of individuals in a catchment over the last years. The second one focuses on the regularity of the reproduction. The EuroDiad 4. 0 database (Barber-O'  Malley et al. , 2022b) , based on this approach, gives the population functionality of diadromous species for many European catchments. Following these criteria, the designation of population functionality for anadromous species (i.
Gathering freshwater-sea information in Western Europe For the current population functionality decision variable, the EuroDiad 4. 0 database (Barber-O'Malley et al. , 2022b) was used (Table  3 ). Population functionality was available for catchments in Europe, the Middle East and North Africa in four abundance categories for the 2011-present time period. The categories are abundant (i. e. , a present and numerically dominant functional population in the freshwater community), common (i. e. , a present functional population in the catchment), rare (i. e. , occasional vagrants were recorded in the basin) and absent (i. e. , the species has never been recorded in the catchment or disappeared from the freshwater community). Categories "abundant" and "common" were classified as "Green" whereas the two others were grouped under "Red" (see definitions in Section 2.
A tool revealing a need for more evidence to support integrative management of diadromous species The application of our framework on four specific catchments was debated with regional experts, both on the decision variable values used for their catchment and the management guidance options obtained. By this process, in the Scheldt River, the population status of A. fallax in the EuroDiad database was reviewed from « rare » to « common », leading to a population functionality classified as "Green". The Scheldt River is a great example of fish (Van den Bergh, pers. Comm. ) and hyperbenthic community recovery  (De Neve et al. , 2020)  in response to local management measures targeting water quality improvement (  Van den Bergh et al. , 2005). In this first stage of the decision tree, no other conflict between database entries and expert knowledge were noted.
Thus, to be more intelligible, colour combinations between the different decision variables obtained from models should be presented as « the most likely scenario » for a given catchment and the starting point for discussions and debates around the freshwater-sea continuum and the impacts of climate change on the overall habitat suitability. To circumvent this issue around data paucity at sea, gathering an international database on diadromous catches and by-catches at sea could be a good solution but it would require strong transnational cooperation and data management plans. Other protocols for the collection of marine distributional data could be considered (e. g. tagging, telemetry and/or eDNA). It would be helpful to apply these methods at a local level to inform on catadromous spawning areas and on growth aggregations for anadromous species at sea.
Acknowledgments This work was funded by the  FutureMARES (Climate Change and Future Marine   Ecosystem Services and Biodiversity ) project funded by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  869300. This study is part of the  Atlantic Area Interreg Project DiadES. Special thanks go to the  Pole MIAME  for allowing the access to the MigrenMer database and marine species distribution model scripts. We are grateful to  Eric Quinton  ( INRAE ) for his help for the calculation of the "distance to the coast" variable. Catarina S. Mateus  and  Joana Boavida -Portugal were funded by the  Portuguese Science Foundation (FCT)  through the  multiannual funding program  contract of the  UID MARE (Marine and Environmental Sciences Centre) , via project  UIDB/04292/2020 , and under the project  LA/P/0069/2020  granted to the  Associate Laboratory ARNET.
% of occurrences or no temporal alignment with the biological response being modelled Loss of <25 % of occurrences and temporal alignment with the biological response being modelled No significant loss of occurrences and temporal alignment with the biological response being modelled Climate change modelling A global climate model Multiple global climate models Multiple regionalised climate models Model performances Model building limits Acknowledgment of uncertainties in model outputs without statistical evaluation Acknowledgment of uncertainties in model outputs with a formal uncertainties analysis Full exploration of uncertainties in model outputs Evaluation metrics adapted to the data and models and predictive capacities evaluated Very low (relative to its scale and ratio of presence to absence; Landis and Koch, 1977) Low (relative to its scale and ratio of presence to absence; Landis and Koch, 1977) High (relative to its scale and ratio of presence to absence; Landis and Koch, 1977) Model validation No attempt to validate the model Simple techniques to validate the model such as validating the model on a subset of the initial dataset Intense techniques to validate the model such as n-fold cross-validation or use of independent dataset Model extrapolation.
Acknowledgements This work was supported by the  Agence Nationale de la Recherche (Program Investissements d'Avenir, VERIFICO (MC) ,  FARCO  projects (AM));  Ministry of Higher Education and Research; Hauts de France Council  ( GPEG  project, (AM));  European Regional Development Fund (Photonics for Society  P4S, (AM, MC and AK)) and the  CNRS (IRP LAFONI , (AM)) and  H2020 Marie Skłodowska-Curie Actions  ( MEFISTA ,  MSCA-713694 , (AM)) and the  University of Lille Through  the  LAI HOLISTIC (AM).
We empirically assess by means of the local projection method, the impact of financial crises on climate change vulnerability and resilience. Using a new dataset covering 178 countries over the period 1995-2017, we observe that resilience to climate change shocks has been increasing and that advanced economies are the least vulnerable. Our econometric results suggest that financial crises (particularly systematic banking ones) tend to lead to a short-run deterioration in a country´s resilience to climate change. This effect is more pronounced in developing economies. In downturns, if an economy is hit by a financial crisis, climate change vulnerability increases. Results are robust to several sensitivity checks.
This paper aims to empirically test these two conflicting propositions and answer whether crises are environmentally friendly or not. A perusal of the literature reveals no such study. Specifically, we empirically assess the impact of (financial) crises on climate change taking advantage of a new dataset of climate change vulnerability and resilience developed by the Notre 1 Given the lockdown observed in many countries as consequence of the Covid19 pandemic, polluting emissions have been falling but many feel it will not fundamentally have a long-lasting impact on climate change. https://www. bbc. com/future/article/20200326-covid-19-the-impact-of-coronavirus-on-the-environment 2 Several papers assessed the output-emissions decoupling hypothesis and how their cyclical relationship has changed over time (see e.
Data Our assesses "a country's capacity to apply economic investments and convert them to adaptation actions". Figure  1  shows the time profile and box-whisker plots for both the vulnerability and resilience indices. We observe that resilience to climate change shocks has been increasing, particularly since the early 2000s. From the bottom charts, we see that advanced economies are the least vulnerable (and more resilient) to climate change. 7 The ND-GAIN database, covering 184 countries over the period 1995-2017, is available at https://gain. nd. edu/. Financial crises come from Leaven and Valencia's (2018) database which was recently updated, and which is publicly available. These include precise dating for (systemic) banking crises, currency crises, debt crises and sovereign debt restructurings.
Acknowledgments We are grateful to  Nathan Benjamin ,  Anthony Charles ,  Kiril Hristov ,  Zohar Komargodski ,  Eric Perlmutter ,  Silviu Pufu , and  Yifan Wang  for useful discussions. This research is supported by the  FWO  projects  G003523N  and  G094523N. NB and JH are also supported in part by the  KU Leuven   C1  grant  ZKD1118 C16/16/005  and by  Odysseus  grant  G0F9516N  from the  FWO. VR is supported by a public grant as part of the  Investissement d'avenir  project, reference  ANR-11-LABX-0056-LMH , LabEx LMH. NB and VR are grateful to the  ENS Paris  for warm hospitality during part of this project. VR is partly supported by a  Visibilité Scientifique Junior Fellowship  from  LabEx LMH  and is grateful to the  CCPP at New York University  for hospitality during the final stages of this project.
Acknowledgments We acknowledge useful discussions and correspondence with  Damir Bečirević  and  Roman Zwicky. This work is supported by  ANR  under contract n. 202650 (GammaRare). Open Access. This article is distributed under the terms of the Creative Commons Attribution License ( CC-BY 4. 0 ), which permits any use, distribution and reproduction in any medium, provided the original author(s) and source are credited.
Acknowledgements This work is part of the project  Dipartimenti di Eccellenza   2018-2022 , of the  Department of Earth and Environmental Sciences  at the  University of Milano-Bicocca , funded by the  Italian Ministry of Education, MIUR. V. A. P. M. acknowledge the  Erasmus Program  for the period in the  Institute des Sciences de la Terre , at the  Université Grenoble Alpes , and the "Tectonics,  Reliefs & Basins  team" support in this last institution. P. S. was supported by the  Italian (Rita Levi Montalcini  grant,  DM 694-26/2017 ) funded by  MIUR. P. G. V. acknowledges funding from the  French ANR -PIA program  ( ANR-18-MPGA-0006 ). Andrés Echaurren  and four anonymous reviewers provided useful comments that helped us improving the manuscript.
Model description The model used for the MPWP simulation is the fully coupled water isotope-enabled version of the Community Earth System Model version 1 (iCESM1. 2)  (Brady et al. 2019 ). The four model components that make up this model are: Atmospheric Model (CAM5. 3, resolution 1. 9° latitude×2. 5° longitude), Land Surface Model (CLM4, 1. 9°×2. 5°), Ocean Model (POP2, ~1. 0°×1. 0°), and Sea Ice Model (CICE4, same resolution as POP2). All the components of iCESM1. 2 make the interactive exchanges via a coupler version 7 (CPL7). The model codes are public open access online (https://github. com/NCAR/iCESM1. 2). As assessed by the work of  Nusbaumer et al. (2017)  and  Brady et al. (2019) , iCESM1. 2 has good performance in representing the observed distribution of stable isotopes of oxygen (δ 18 Op) in precipitation, and thus it is widely used in paleoclimate simulations  (Zhu et al.
2022). We used the similar warm model initialization (same salinity and slightly higher ocean temperature) for iCESM1. 2 and CESM1. 2 to expect a higher magnitude of MPWP surface warming that was underestimated by the models. For detailed initial values of temperature and salinity, please refer to online ts_init_b20. 681_1100-01_s_35. 60level. dat. orig (source: https://github. com/CESM-Development/paleoToolkit/blob/master/cesm1/ocn/ic/ts_init_b20. 681_1100-01_s_35. 60level. dat. orig) In addition, cause on the differences between simulations were examined by decomposing surface MPWP warming into several physical processes following the work of  Lu and Cai. (2009). 4𝜎𝑇 𝑠 3 ∆𝑇 ≈ -∆𝛼 (𝑆 ↓ + ∆𝑆 ↓ ) + ∆𝐶𝑅𝐹 𝑠 + (1 -𝛼)∆𝑆 ↓,𝑐𝑙𝑟 + ∆𝐹 ↓,𝑐𝑙𝑟 -∆𝑄 -∆(𝑆𝐻 + 𝐿𝐻) (1) Where overbar indicates time mean,𝑆 ↑ and 𝑆 ↑ are surface downward and upward shortwave radiation fluxes, 𝛼 is the surface albedo calculated as the ratio of 𝑆 ↑ to 𝑆 ↑ at surface, F is net longwave radiation fluxes at surface derived from differences between 𝐹 ↓ (downward    -f-i ) and CESM1.