dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements. We gratefully acknowledge the funding of the  German Research Foundation (Deutsche Forschungsgemeinschaft, DFG)  to in its priority program " Fusion of Radar Polarimetry and Atmospheric Modelling"  ( SPP-2115 , PROM), specifically the project  PARA  ( FKZ QU 311/21-1 ). JQ further acknowledges funding by the  EU Horizon 2020 project  " FORCES " ( GA 821205 ). This work used resources of the  German Climate Computing Centre (Deutsches Klimarechenzentrum , DKRZ) granted by its  Scientific Steering Committee (WLA)  under project ID. We thank the  AERIS/ICARE Data and Services Center  for providing access to the DARDAR and 2B-CLDCLASS data used in this study.
Data availability. The ICON-AES data are stored at the German climate computing center (DKRZ) and are available upon request to the corresponding author. The 2B-CLDCLASS CloudSat data and DARDAR dta are downloaded from the ICARE Data and Services Center (https://www. icare. univ-lille. fr/asd-content/archive/) Code and data availability. The source code of this study is available at https://zenodo. org/records/10039734. Updates of the code can be found at https://github. com/shoernig/code_subgrid_scale. Informations about the liscence and the respective README files are also included. Access to ICON source code can be obtained at (https://code. mpimet. mpg. de/projects/iconpublic/wiki/How_to_obtain_the_model_ code) after accepting a license agreement.
The main purpose of the present study is to investigate impact of cross-populational variability of survival on the estimates of time-integrated radiation risk. For this, the evaluated data and lifetables from the Human Mortality Database  [23]  were used to represent global variability of survival curves. The lifetables, found in the Human Mortality Database, contain the evaluated data, derived from national demographic statistics, and are homogeneously represented at various age and time intervals up to age 110. For the present study, the 1Â1 period lifetables were used for 41 countries, as listed in Table  A. 1. The survival curves for different populations were converted to the hazard scaling factors represented as a country-specific time series of ratios of log-transformed survival and log-transformed median of survival data for a given age: R i a ð Þ ¼ ln S i a ð Þ ln med S a ð Þ ð Þ ð Þ : ð6Þ The randomized survival function S r is computed by randomly picking a series of the hazard scaling factors R r (a) and modifying the survival curve S(a) for the selected target population as follows: S r a ð Þ ¼ exp R r a ð Þ lnS a ð Þ ð Þ : ð7Þ Use of the scaling factors provided a non-parametric modelling of the variability of hazard (cumulated mortality) across populations by randomly picking a time series of the hazard scaling factors and modifying the survival curve used for radiation risk computations.
Variability of survival curves across populations The survival curves for both sexes were derived from the period lifetables for 41 countries presented in the Human Mortality Database  [23]  and the survival data for each country were averaged for 10 most recent years available at the date of query. The resulting survival curves are shown in Fig. 1. As seen from the figure, the variability of the average survival curves between populations is substantial, especially, for males. For example, the survival at age 60 varies from 0. 61 to 0. 93 for males and from 0. 85 to 0. 96 for females, at age 70 from 0. 39 to 0. 84 for males and from 0. 72 to 0. 91 for females, and at age 80from 0. 17 to 0. 64 for males and from 0. 44 to 0. 80 for females. The distributions of the survival values for different countries at the given age are asymmetric and skewed, thus no country exists which survival curve can be regarded as representative for a central or median value for the whole dataset in the full range of ages from zero to the maximum lifetime.
Radiation risk per unit dose Performance of different risk metrics was compared using the values of lifetime (LAR, REIC, ELR, RRL) or timeintegrated (RADS R , RADS M ) radiation risk per unit dose computed with the radiation risk models either linear (ASC) or linear-quadratic (LEU) on dose. These values were calculated for males and females exposed at ages 35 and 60 to different doses in range from 0. 01 to 3 Sv. The risk values per unit dose for age at exposure 35 are shown in Figs. 3  and 4  for the endpoint ASC and in Figs. 5  and 6  for the endpoint LEU. Each plot in Figs. 3-6 consists of two sublots showing compatible risk metrics: LAR, REIC and ELR are shown on the left, while RADS M , RADS R , and RRL are shown on the right. As indicated above in sub-section 2. 3, RADS R was calculated for the retirement age of 65 years, while RADS M for the mean outcome-free lifetime, which for the selected population data (Germany) for the endpoint ASC was equal to 74.
Relationship between the cohort and period life expectancy is not straightforward and may depend on social, economic, medical and political factors. Discussion on this relationship and examples of secular changes of cohort survival can be found elsewhere  [26] [27] [28] [29]. In the present paper, the period lifetables for different countries were used to model variability of survival curves among different populations with different life expectancy and total mortality rates. To better characterize effect of additional uncertainty due to interpopulation variability of survival the radiation risk values were calculated for ages at exposure 35 and 60. While modelling this uncertainty, it was shown that asymmetric distributions of the survival data resulted in negative biases (down to À10%) of the mean and median risk per unit dose values for both ASC and LEU endpoints, using survivalbased risk metrics (LAR, REIC, ELR, RRL).
, 2013). This approach uses a series of gaussians placed at the same location as expected for various pigment absorption peaks and minimizes the difference between a spectrum constructed of these gaussians with the measured spectrum. This gives the approximate concentration of a range of pigments that can be used as a proxy for various phytoplankton groups. This data was collected using Inlinino  (Haëntjens & Boss, 2020)  an open-source logging and visualization program, and processed using InlineAnalysis (https://github. com/OceanOptics/InLineAnalysis) following  (Boss et al. , 2019) , further details in Text S1.
Acknowledgments Funding support was provided by  National Aeronautics and Space Administration (NASA) Future Investigators   in NASA Earth and Space Science and Technology (FINESST , # 80NSSC19K1366 ,  Ocean Biology and Biogeochemistry program ) and the  Zuckerman STEM Leadership Program  to PCG. R/V Shearwater ship time was supported primarily by  ONR-NRL program  element  61153N ,  WU 72-1R25  to IS and partially by the  Nicholas School of the Environment and Duke University Marine Lab  donors through a student research grant to PCG. The authors thank the crew of the  R /V Shearwater ,  Matt Dawson ,  Tina Thomas ,  Zach Swaim , and  John Wilson , and the captain of  F/V Instigator ,  Josh Wentling. We thank  Ali Chase  for helpful discussions of her gaussian decomposition algorithm for phytoplankton pigments.
, 2022). It should be noted that the size distribution measurements were conducted at the WAI, thus, the interpretation of the presented time series of aerosol particles during those high-wind velocity times is limited. In order to precisely identify such an impact, more intensive measurements need to be conducted by, e. g. , having aerosol particle size distribution measurements at the rooftop and in the laboratory simultaneously. 660 Testing of quartz fiber and polycarbonate filters A subsample of the datasets was designed to test a possible influence of using different filter materials (quartz fiber versus polycarbonate filters). For this comparison, HERA and the standard sampler from TROPOS were operated in parallel using different filter materials. For the analysis, INDA and LINA, both operated at TROPOS, were used for evaluation.
https://doi. org/10. 5194/egusphere-2023-1125 Preprint. Discussion started: 15 June 2023 c Author(s) 2023. CC BY 4. 0 License. conditions, and hence cloud droplet and ice crystal activation, should be performed in future studies. More such intensive INP intercomparisons, resulting in a larger dataset, should be conducted in the future to better understand discrepancies amongst the online instruments and to guide potential technical mitigations. INP filter sampling was performed during day-and night-time for 8 hours and analyzed with FRIDGE, INDA, IS, 735 INSEKT, LINA, LINDA, and UNAM-MOUDI-DFT. The filters for IS and LINDA were collected directly in ambient air on the rooftop of the laboratory, while the other filters were collected behind the WAI in the laboratory.
, 2015). Therefore, satellite ozone profile datasets from infrared instruments, in combination with offline ozone radiative kernels (e. g. Bowman et al. , 2013; Rap et al. , 2015) , can be used to quantify the PD TO 3 RE. This can then either constrain model estimates of PD TO 3 RE or be used directly with modelled PI TO 3 RE to derive the TO 3 RF. Several studies have previously used satellite data to derive short-term estimates of the TO 3 RE (i. e. from a few months of data). Joiner et al. (2009)  used tropospheric column ozone (TCO 3 ) data based on two satellite instruments, Ozone Monitoring Instrument (OMI) and Microwave Limb Sounder (MLS) measurements, also known as the OMI-MLS product, for January and July 2005, to estimate the resultant instantaneous TO 3 RE at the tropopause to be 1.
Ozonesondes Despite the three IASI ozone profile products using the same radiance data, the three retrieval schemes produced systematic differences between the products in the long-term TCO 3 average (e. g. Sect. S1 and Figs. S2 and S3 from the Supplement). However, the spatial structure in the three products compares well. Therefore, to harmonize the three IASI TCO 3 datasets (i. e. absolute values but not long-term variability) we use ozonesonde data from the World Ozone and Ultraviolet Radiation Data Centre (WOUDC; WOUDC, 2023), the Southern Hemisphere ADditional OZonesondes (SHADOZ; SHADOZ, 2023) project and the Global Monitoring Laboratory, National Oceanic and Atmospheric Administration (NOAA; NOAA, 2023). Here, O 3 measurements were rejected if the O 3 or pressure values were unphysical (i.
For the application of the AKs to the ozonesonde profiles, the full ozone profile is required which is not available from the ozonesondes (i. e. mid-stratosphere and above). Therefore, the ozonesonde profile above its minimum pressure level is extended using the a priori profile from the corresponding satellite product. The profile is smoothed vertically across the joining pressure level to avoid a profile discontinuity. Once the ozonesondes had been co-located with the satellite data (i. e. within 6 h and 500 km) and the AKs applied, the two datasets were compared across the full 2008-2017 period. We typically find a global annual TCO 3 systematic bias of 14. 9 %, 2. 7 %, and 17. 4 % for IASI-FORLI, IASI-SOFRID, and IASI-IMS, respectively, which is consistent with  Boynard et al. (2018) ,  Barret et al.
g. as in  Rowlinson et al. , 2020; Pimlott et al. , 2022). Annually varying anthropogenic emissions come from the Coupled Model Intercomparison Project Phase 6 (CMIP6;  Feng et al. , 2020). Climatological biogenic emissions are from the Chemistry-Climate Model Initiative (CCMI;  Morgenstern et al. , 2017) , but isoprene and monoterpene emissions vary annually from the Joint UK Land Environment Simulator (JULES;  Pacifico et al. , 2011)  within the free-running UK Earth System Model (UKESM;  Sellar et al. , 2019)  from a CMIP6 historical setup. Other natural emissions come from the Precursors of Ozone and their Effects in the Troposphere (POET;  Olivier et al. , 2003)  and biomass burning emissions from the Global Fire Emissions Database (GFED) version 4 (van der Werf et al. , 2017). For methane (CH 4 ), the model tracer is scaled to the annually varying global averaged surface CH 4 value from NOAA  (Dlugokencky, 2020).
Evaluation of the model using the IASI products and ozonesondes (see Sect. S2, Figs. S3, S4) shows the model generally captures the TCO 3 spatial pattern and absolute values. In the tropics (middle and high latitudes), the model underestimates (overestimates) by approximately 10 %-20 % on average. These biases are comparable with other modelling studies evaluating models against satellite TO 3 observations (e. g. Archibald et al. , 2020; Monks et al. , 2017; Nassar et al. , 2009; Young et al. , 2013) , indicating that TOMCAT is suitable for this study. The global mean TCO 3 from TOMCAT (2008-2017) (Fig. 2  -top panel) is 30. 7 DU and consistent with the IASI datasets in Fig. 1. When translated into TO 3 RE, described above, the peak values from TOMCAT range between 2. 0 and > 2. 5 W m -2 over Africa, Australia, and the sub-tropics.
Data availability. The IASI-FORLI and IASI-SOFRID data can be obtained from https://iasi. aeris-data. fr/O3 (IASI-FORLI, 2020) and https://iasi-sofrid. sedoo. fr/ (IASI-SOFRID, 2022). The IASI-IMS data are available via the NERC Centre for Environmental Data Analysis (CEDA) JASMIN platform subject to data requests. However, we have uploaded the data (IASI-IMS, 2022; RAL, 2024a-e)  used in this study to Zenodo for 2008-2009 (https://zenodo. org/records/10699293), 2010-2011 (https://zenodo. org/records/10765615), 2012-2013 (https: //zenodo. org/records/10765944), 2014-2015 (https://zenodo. org/ records/10766366), and 2016-2017 (https://zenodo. org/records/ 10766682). The TOMCAT ozone data can be found at https://zenodo. org/records/10670324 (Pope et al. , 2024). The ozonesonde data for WOUDC, SHADOZ, and NOAA are available from https://woudc.
Geomorphological setting The most important consideration for selecting sites for Paleo±Dust is that the lithogenic fraction of the sediment be dominated by atmospheric deposition, or that the atmospherically derived lithogenic fraction may be estimated quantitatively. The site's geomorphological setting exerts a primary control on this. Sites of loess deposits with a massive structure reflective of deposition of dust from the atmosphere are included in the database, while loessoid sites consisting of reworked loess by either fluvial or slope processes are excluded (e. g. ,  Pye, 1995). Peat usually consists of an upper ombrotrophic section where lithogenic materials are exclusively supplied by the atmosphere, and a lower minerotrophic section where lithogenic materials are also supplied laterally by groundwater (e.
e. , ttop and tbottom, respectively, in thousands of years before present, with two decimal places), DMAR_g/m2/a and DMAR-1sigma_g/m2/a (i. e. , mean DMAR and 1-σ εDMAR for the defined age bracket, respectively, in g m -2 a -1 , with three significant digits), and DMAR10_g/m2/a and DMAR10-1sigma_g/m2/a (i. e. , mean DMAR10 and 1-σ εDMAR10 for the defined age bracket, respectively, in g m -2 a -1 , with three significant digits). The two supporting text files each contain 24 variables, corresponding to the same 10 variables as the main files plus 14 extra variables: first_appeared_in_dataset (i. e. , reference to compilation where site first appeared), top-age-1sigma_kaBP and bottom-age-1sigma_kaBP (i. e. , 1-σ εt top and εt bottom , respectively, in thousands of years before present, with two decimal places), thickness_m (i.
Acknowledgements We thank  D. Muhs ,  D. Constantin ,  T. Stevens ,  S. Pratte , and  G. Le Roux  for providing us with highly valuable data. This research was financed by project  ANID-FONDECYT-POSTDOCTORADO2020-3200085  and by a  2022   INQUA Fellowship , both awarded to NJC, and by project  ANID-FONDECYT-REGULAR2023-1231682  awarded to FL. The discussions that resulted in this paper also benefited from meeting travel grants from project  CLIMAT-AmSud 22-CLIMAT-01.
https://doi. org/10. 5194/essd-2023-241 Preprint. Discussion started: 14 August 2023 c Author(s) 2023. CC BY 4. 0 License. 5 Conclusions Paleo±Dust is an updated global paleo-dust deposition rate compilation for mean pi-HOL and LGM climate states that includes quantitative estimates of uncertainties. Paleo-dust flux measurements from peat bog cores are included in a paleodust compilation for the first time. By excluding deglaciation dust fluxes, Paleo±Dust better isolates mean interglacial and glacial dust fluxes than previous datasets. Site-specific age brackets allow to sub-sample mean dust deposition constraints for specific time windows of interest. Grain size information is extracted from the original studies to derive fluxes of dust with <10-µm diameter particles.
Acknowledgements. This work was funded by the  SEAMLESS  (" Services based on Ecosystem data AssiMiLation: Essential Science and Solutions" ) project which has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement No  101004032. The calculations were performed using HPC resources from  GENCI-IDRIS  (grant  A10-011279 ). The perceptually.
Code availability. The ensemble simulations have been performed with NEMO4. 0, including stochastic parameterizations, as provided through the SEAMLESS project (doi:10. 5281/zenodo. 6303007). The ensemble observational update (with the LETKF algorithm and the MCMC sampler) and the computation of the probabilistic scores have been performed using the EnsDAM (github. com/brankart/ensdam) and SeSAM softwares (github. com/brankart/sesam), using a set of shell scripts to perform the various operations (github. com/brankart/ensembleocean-colour).
uniform colour-maps 'tofino' and 'roma' (developed by F. Crameri, doi: 10. 5281/zenodo. 2649252) is used in this study to prevent visual distortion of the data. 35 https://doi. org/10. 5194/egusphere-2023-2026 Preprint. Discussion started: 14 September 2023 c Author(s) 2023. CC BY 4. 0 License.
Analytical methods Elemental analysis was performed using a CHNS elemental analyser (Flash EA 1112 Series) to determine the elemental content of carbon (C), hydrogen (H), nitrogen (N) and sulfur (S) present in the raw material. The ash content was obtained by combustion of the sample in a furnace at 900 °C for a period of 4 h  [12]. However, the fixed carbon percentage was estimated by the mass difference  [13]. The High Heating Value (HHV) was estimated based on the elemental analysis data (CHN-O) using Sheng and Azevedo's correlation, as below  [14] : HHV = -1. 3675 + 0. 3137 × C + 0. 7009 × H + 0. 00318 × O (MJ/kg) (1) Thermogravimetric analysis was carried out to characterize the degradation behaviour of the different agro-food wastes using a TGA analyzer (SETSYS Evolution 1750). The TGA tests were performed in an inert atmosphere from ambient temperature to 700 °C at the heating rate of 10 °C/min.
g. Mackay et al. 2011). While the isotopic composition of precipitation provides useful information, it is often not available for field studies and few monitoring studies of precipitation and lake water isotope compositions in combination with δ 18 OBSi do exist  (Kostrova et al. , 2019; Hernandez et al. , 2010). A possible workaround can be derived from the GNIP database (IAEA/WMO, 2022) and spatial interpolations thereof, e. g. The Online Isotopes in Precipitation Calculator  (Bowen, 2017).
Consequently, no study has yet empirically linked the signal properties of δ 18 OBSi records and lake basin parameters in a harmonized dataset. In order to overcome these gaps, this paper aims at providing a comprehensive compilation and combined statistical evaluation of the lake sediment δ 18 OBSi-records published to date. We accomplish these objectives by means of the following working steps: 1) collecting available lake sediment δ 18 OBSi-records published to date, 2) complementing these records with the individual lake basin parameters and 3) assessing the signal properties of δ 18 OBSi records with regard to lake basin parameters, in order to 4) identify common spatio-temporal patterns and trends in the δ 18 OBSi signals. This effort shall lead to a better understanding of the general constraints for interpreting lake sediment δ 18 OBSi-records and make these records more readily usable also for proxy-model comparisons.
Methods This study follows a three-stage approach: the first stage (data acquisition) comprises identifying lacustrine δ 18 OBSi datasets and publications published to date. A second stage includes acquiring the actual δ 18 OBSi datasets and archiving them in a standardized format. Where appropriate, the identified published datasets were arranged into longer continuous records. This includes records from the same sediment core published in different manuscripts and records from different sediment cores from the same lake but similar locations. The third and final stage (record analysis) interprets the actual lacustrine δ 18 OBSi isotope records with respect to their hydrological and geographical constraints in order to identify possible common trends and signal properties of all isotope records or smaller spatially or temporally constrained datasets.
Identification and acquisition of the datasets and publications in this work followed an additive approach, i. e. thorough literature survey. We have chosen this approach due to the limited number of laboratories and working groups worldwide measuring δ 18 OBSi. In this study, we focused exclusively on down-core records from lacustrine sediments. Identified publications and datasets were entered into a uniform metadata table giving one entry to each publication and each dataset. If more than one publication was written about the same sediment core, each of these publications was given a separate entry. Likewise, if one publication presents data from more than one sediment core, each of the sediment cores was given a separate entry to the database. For each of these entries, metadata on coring procedure, hydrological setting and chronology were supplemented.
Data were extracted from the corresponding publication(s), from public repositories or directly from the authors. Hydrological parameters such as catchment area, average depth and tres were additionally obtained from the HydroLAKES database  (Messager et al. , 2016)  and stored as separate variables. For a detailed description on how the values in the HydroLAKES database were created, we refer to  Messager et al. (2016). This procedure was necessary because original publications do not always specify all of these parameters and because parameters supplied in original publications may not be consistent. For further analysis and for linking the individual isotope records with lake basin parameters, the HydroLAKES database entries were given preference. Original publications were only used for lakes where no data were available from the HydroLAKES database.
The parameter "maximum water depth" was always taken from the original publications since this parameter is not provided by the HydroLAKES database. While the geo-statistical approach of the HydroLAKES database may not provide the most precise values for individual lake basin characteristics, it ensures comparability among the different lake basins analyzed in this study. It also prevents potential issues arising from different authors providing conflicting values for the same lake basin (i. e. Lake Baikal, Lake El'gygytgyn). Isotope datasets for each of the entries were archived in separate tables specifying depth, age and measured isotope values. Datasets were taken directly from the respective publication, whenever possible. If datasets were not available, within the original publications or as supplements thereof, public repositories (such as www.