dataesr/scientific-paragraphs-categorization huggingface dataset:
Global data overview and representativeness The extended global database including all literature data and the aggregated MEMO 2 data consists of 13313 and 4337 measurements of δ 13 C and δ 2 H, respectively, from 64 countries. The map in Fig. 4  shows the partitioning of the data per country, and Table  1  the number of records per CH 4 source. The maps in Fig. A2  of the supplementary material illustrate the increase in number of measurements of each isotopologue. The number of measurements made in fossil fuel reservoirs and compiled in the database by  Sherwood et al. (2021)  is comparatively larger than from studies of other CH 4 emission sources (Table  1 ), and the amount of measurements is not evenly spread geographically: significantly more measurements were made in North American and European countries, Australia, Brazil and Japan.
Global data distribution The global distribution of CH 4 isotopic signatures in the complete extended database is shown in Fig. 5. The values were grouped in categories that correspond to the largest reported emissions  (Saunois et al. , 2020). The categories agriculture, waste, wetlands, and partly other natural are all of modern microbial origin, mainly following the fermentation pathway  (Milkov and Etiope, 2018). They show a normal distribution, and an overlap between the categories, except for the waste sources that are more enriched in 13 C. This difference is particularly visible in the MEMO 2 data, and from a relatively large number of sites from waste related sources. As mentioned in section 2. 4, additional parameters control the isotopic signature of the emitted CH 4 , such as the type of substrate, the presence of oxygen, or secondary (e.
g. oxidation) processes. We recommend to separate the waste category from the other microbial sources to minimise the uncertainty in the assigned isotopic signature. The fractionation factors derived for CH 4 microbial oxidation are larger for δ 2 H  (Coleman et al. , 1981; Bergamaschi et al. , 1998; Chanton et al. , 2006 ), but we don't clearly see an influence of these additional parameters on the δ 2 H CH4 signatures of our dataset. Indeed, the range of δ 2 H signatures for waste is the same for as agriculture and wetlands (Fig. 5 ), but these are based on few measurements compared to δ 13 C (42 % of all measured waste sources reported δ 2 H signatures). The relation between δ 2 H CH4 from wetlands and the δ 2 H H2O from precipitation has been established  (Waldron et al. , 1999; Chanton et al.
δ 2 H CH4 isotopic signatures from pyrogenic sources cover a wide range of values, and overlap with the ones of fossil fuels. Data on δ 2 H H2O could help to parametrise the biomass burning δ 2 H CH4 in more detail  (Vigano et al. , 2010) , similar to the above mentioned relation between δ 2 H CH4 and δ 2 H H2O  (Waldron et al. , 1999; Chanton et al. , 2006; Röckmann et al. , 2010; Douglas et al. , 2021). This database, mean ± sem -44. 5 ± 0. 5 / -50. 7 ± 1. 3 1 -183 ± 3 / -210 ± 5 1 1 for natural gas/coal; 2 also in  Lassey et al. (2000) ;  Houweling et al. (2000) ;  Bousquet et al. (2006); Thompson et al. (2018)  Fugitive CH 4 emissions from fossil fuels cover a wide range of isotopic signatures: δ 13 C from -72. 5 to -18. 3 ‰ and δ 2 H from -349 to 14. 0 ‰. The average δ 13 C of all fugitive CH 4 emissions from the exploitation of fossil fuels in the European Methane Isotope Database was -44.
6 ± 0. 4 ‰ (n=452), and the weighted average was -46. 6 ± 1. 8 ‰ according to the relative emission from conventional and coal fuels production worldwide 7. Our averages are lower than δ 13 C values used in global models, and than the mean of the global database (Table  3 ). But the value of -44 ± 0. 7 ‰ suggested by  Schwietzke et al. (2016) , based on the database from  Sherwood et al. (2017)  scaled with the fossil fuel production in the different regions, is relatively close to our average. The mean values we calculated in  The updated database is beneficial for deriving a representative concept of the isotopic composition of CH 4 sources, but it is important to note that applying appropriate weighting arithmetic is essential. Users need to define the dominant CH 4 sources impacting an area, as well as the relative source type emission rates.
Emission inventories provide such estimates, but top-down approaches are essential to identify potential biases and evaluate the bottom-up approaches  (Alvarez et al. , 2018 ; Etiope and 300  Schwietzke, 2019; Rutherford et al. , 2021; Stavert et al. , 2021). This study presents an updated dataset of isotopic source signatures of CH 4 from recent atmospheric measurements, while including additional data from published literature which were not previously included. The new data is a contribution from the European Methane Isotope Database, that results from the sampling activities performed within the MEMO 2 project. It represents a substantial contribution to the global dataset for fugitive fossil fuels and waste sources, mainly sampled in urban areas. We have highlighted two main improvements in our understanding of the CH 4 isotopic composition: (i) A more robust range of values for modern microbial sources, and a better characterisation of the δ 13 C enrichment in CH 4 from waste sources.
(ii) Fossil fuel related sources could have more depleted values than previous estimates used in global models. In this respect, our data confirm the analysis made by  Schwietzke et al. (2016). Finally, the new European data contain comparatively more δ 2 H measurements. In the case of fossil fuel emissions, the use of δ 2 H CH4 is of particular interest. In general, utilizing both δ 13 C and δ 2 H for CH 4 improves our ability to clearly separate fossil fuel and microbial sources, compared to δ 13 C alone. The use of δ 2 H as additional constraint could help to answer open questions regarding the CH 4 global budget. To better understand the drivers of δ 2 H variability (except for δ 2 H of precipitation), more measurements are required, especially of pyrogenic and waste sources. The present dataset can be used for CH 4 source attribution, and also to derive global signatures for the different types of emissions.
Data availability The database is made freely available to the scientific community in the belief that it provides the most complete picture of the stable isotopic composition of CH 4 sources. The free availability of these data does not constitute permission for publication of the data. For research projects, if the data used are essential to the work to be published, or if the conclusion or results largely depend on the data, co-authorship should be considered. Full contact details and information on how to cite the data are given in the accompanying database. The database is currently stored in a publically available repository: https://doi. org/10. 24416/ UU01-4PO56T  (Menoud et al. , 2021a). (2017, 2021) , additional literature we found, and the European Methane Isotope Database, for δ 13 C (left) and δ 2 H (right) of CH4 separately.
for non-fossil data. TSherwood et al. ( , 2021))ves concern only values for δ 13 C and δ 2 H of emitted CH 4 , and do not include measurements of other gases or isotope signatures thatSherwood et al. (2017)reported in the fossil fuel database. The variables of interest are listed in Table1 and 140include the site description (country, region, group, category and sub-category) and the δ 13 C and δ 2 H of CH 4. We grouped the sources reported in the European Methane Isotope Database by region and sub-category in order to integrate it in the literature database. We kept the categories and sub-categories as defined inSherwood et al. (2017Sherwood et al. ( , 2021)), but when the new entries from MEMO 2 measurements and published literature required it, we added additional source categories or sub-categories.
In the global database, most fossil fuels records (83. 5%) have δ 2 H CH4 values >-250 ‰. The few values of δ 2 H <-300 ‰, indicating microbial fermentation as gas origin, were found in some coal formations in the United States and Canada. Figure5still allows us to generally conclude that δ 2 H measurements are more reliable to distinguish fossil fuel vs. biogenic CH 4 sources at the global scale than δ 13 C only, which further emphasises the need for more δ 2 H CH4 measurements.
Therefore, the jump-diffusional behavior of Γ1(Q) indicates that each step of the global jump diffusion of the entire lipid molecule is resolved here. In the case of DMPC MLB45 at 283 K and 311 K, the Γ1(Q) values might be constant over the Q-range measured, suggesting that these motions are described by a diffusion on a linear segment or by rotational diffusion  [26]. Since it is not possible to distinguish the type of motions due to the limited data points and large error bars, analysis was conducted for both types of motions for these two conditions. The radius of the confined space (a), which is related to the center-of-mass diffusion, was estimated from the Q0 value, at which Γ1(Q) begins to increase, based on the relation a = π/Q0  [27]. Since some data points were missing for DMPC MLB45 due to the experimental (a) (b) (c) (d) (e) (f) configuration, the a values were estimated for the other three samples, which were found to be 2.
The upper and lower values were obtained from the minimum and maximum values of the score for each parameter, calculated using the DRASTIC index formula. It can take a maximum value of 230 (100 %) and a minimum value of 26 (0 %). The definition of the classes based on index values relies on recommendations from  Aller et al. (1987). The mapping approach allowed a data base of the physical properties of the studied area to be put together, in order to produce a vulnerability map that represents the spatial distribution and variation of vulnerability indices across the area.
Methods and materials To carry out the vulnerability map of the Wadi Nil alluvial aquifer, the empirical approach of the DRASTIC method, developed by the US Environmental Protection Agency (EPA) in 1985, was adopted. It is based on seven parameters  (Aller et al. , 1987) : Depth of the water table (D), Annual net pure Recharge (R), Aquifer lithology (A), Soil type (S), Topography (T), Impact of the vadose zone (I), and Aquifer hydraulic Conductivity or permeability (C). The impact of the vadose zone is the function of lithology of the unsaturated zone. Vulnerability indices are calculated by the sum of the partial DRASTIC indices (I) of each parameter. (Aller et al. , 1987)  by compiling geospatial data and calculating DRASTIC indices. The data used to conduct this study are from the April 2012 season, which corresponds to the high-water period.
Hydraulic Conductivity of The Aquifer It refers to the speed of water movement in the saturated zone. The DRASTIC method assigned a weight of 3 to hydraulic conductivity or six classes are weighted by a coefficient varying from 1 to 10. In the case of the deep water table, the hydraulic conductivity values used in this study were obtained from pumping test data from boreholes distributed across the plain. However, for the shallow water table, the permeabilities used represent the geological formations that appear in the first 15 m (Table  2 ). The hydraulic conductivity indices for the shallow water table (Figure  13.
Acknowledgment This work was carried out in collaboration with the  National Water Resources Agency (ANRH)  and the  Water Resource Directorate (DRE) of the wilaya of Jijel (Algeria)  and funded by  General Directorate of Scientific Research and Technological Development (DGRSDT). The authors would like to thank these two organizations for having kindly made available to us all the data and information necessary for the elaboration of this study.
Another possibility is that the relatively mild stressor exposure in DynaCORE-L, which occurred after the first wave of the pandemic had subsided, permitted homeostatic coping. Owing to its longer time range and more severe at-risk constellation, DynaM-OBS should be more sensitive than DynaCORE-L in detecting allostatic processes. At the same time, DynaM-OBS allows us to test the generalizability of the baseline resilience factors discovered in DynaCORE-L to a different sample. Another source of hypotheses is the unpublished preliminary analyses of the MARP and LORA data sets. In synopsis with the anticipated results from these long-term studies, DynaCORE-L and DynaM-OBS will yield a good picture of the temporal dynamics of allostatic resilience processes. DynaCORE-L had further detected that week-to-week changes in stressor exposure predate subsequent changes, specifically in the resilience factor of active behavioral coping, which were, however, not translated into improvements in SR.
Statistical Methods Development The conceptual framework and the types of data generated by DynaM-OBS require new analysis methods. Although the time-sensitive regression models used in DynaCORE-L were appropriate for analyzing time point-to-time point (week-to-week) effects, they were also limited. Notably, they only tested each of the multiple, partially correlated resilience factors in a separate model, and they did not consider interrelations between resilience factors; they did not test for time-lagged effects between either stressors and resilience factors or resilience factors and SR extending beyond 1 week; and they did not test for changes or change points in the time series of stressor exposure, resilience factors, or SR. Therefore, we also use DynaM-OBS (together with the other mentioned studies) as a test bed for new methodological developments.
These include regularized regression methods  [36, 43] , network models  [12] , landmarking methods  [13] , and individualized deep dynamic methods in combination with deep generative models, allowing, for example, additional quantification of resilience, multiple imputation, and dimension reduction  [43]. We will also use the data for potential improvements in the calculation of SR scores, including partial least squares methods  [44] , serving notably to improve the mental health problem variance explanation by stressor exposure and the better integration of stressors of various sources into stressor exposure  [13]. Thus, DynaM-OBS has a strong exploratory character from a methodological perspective.
Neuroimaging In the development of PASTOR, we have emphasized the problems associated with self-report assessment in general (which knows various sources of bias) and self-report assessment of appraisal processes and contents in particular (not all of which may be accessible to consciousness and be verbally reportable)  [9]. Therefore, we also use a complementary approach, namely, to indirectly index the effectiveness and efficiency of participants' positive appraisal processes through objective measures of their behavioral, physiological, or neural reactions to stressors in the laboratory or in real life. To this end, we generated a baseline neuroimaging battery where fMRI tasks are (partly) accompanied by behavioral and physiological recordings. The battery includes tasks from the corresponding MARP battery (reward sensitivity, differential fear conditioning, and situation-focused volitional reappraisal  [37] ), all of which provide reproducible imaging data  [38]  and were either found to be predictive of SR in initial analyses in MARP (unpublished) or were retained because of their special theoretical interest (refer to the study by Kampa et al  [37]  for task rationales in the context of PASTOR).
Finally (f), we hypothesize that SR 1 will inversely correlate with amygdala-ventromedial prefrontal functional connectivity during implicit emotion processing as measured using a faces versus shapes matching paradigm  [50]. For details on contrasts, refer to the studies by Kampa et al  [37]  and Wackerhagen et al  [50]. Similar to the questionnaire measures, PASTOR-related neuroimaging measures will be additionally analyzed using multivariate regression and factor analysis to identify best or latent predictors, respectively, of SR. We will also attempt cross-modality analyses including both questionnaires and neuroimaging measures. Similar to longitudinal resilience data, the neuroimaging data will be used for the development of analysis methods.
Other Data Modalities and Hypotheses PASTOR claims that the effects of other social, psychological, and biological resilience factors on resilience are mediated by how they shape PAS  [9]. In this framework, we used a range of additional questionnaires, neuropsychological tests, and biological samples (blood for genotyping, DNA methylation, plasma proteome analysis, and also cytokines at 1 site; stool for gut microbiome analysis; and saliva for cortisol analysis). Well-known risk factors and sociodemographic variables were also assessed. The latter will be included as covariates in several planned analyses, along with other potentially confounding factors such as the study site.
Aims of This Study DynaM-OBS targets a gap in resilience research: providing a dense longitudinal database to assess resilience as a long-term outcome and the underlying resilience processes. This approach and framework offer new possibilities to test current hypotheses about putative resilience factors as well as to explore novel ones. Specifically, our main hypothesis is that a PAS is both contemporaneously and prospectively predictive of resilience, operationalized as SR. Moreover, we expect several secondary predictors of resilience, ranging from specific brain network configurations during acute stress to high reward sensitivity, threat safety discrimination, and volitional reappraisal processes, among others.
Strengths and Limitations Despite its strengths, which are mainly reflected in the study's richness of longitudinal and multimodal data on resilience factors, processes, and outcomes, DynaM-OBS also has several limitations. Although we were trying to be exhaustive in our assessment of stressors (including potential life events, a large number of microstressors in a wide range of life domains, and pandemic-related stressors), we cannot exclude the possibility that some relevant types of adversity were missed. For example, we did not collect information on parental traumatic exposure, which may transmit to children, and did not ask specific questions on the exposure of participants to stressors that only became more apparent over the course of data collection (eg, Russia-Ukraine war). Moreover, because of the multicenter nature of our study, we could not entirely rely on previously validated questionnaires for each study site's language and therefore translated some questionnaires ourselves, without validating them.
This may pose a threat to the construct equivalence and validity between the nonvalidated and validated versions of the questionnaires. Another consequence of the multicenter nature of DynaM-OBS is that different COVID-19 restrictions applied at the different sites. Among other things, this led to the interruption of participant inclusion at Charité-Universitätsmedizin Berlin from mid-December 2020 to the end of February 2021, although all other study sites continued participant inclusion. For the interested reader, we present an overview of the general COVID-19 situation at all sites over the course of data collection in Multimedia Appendix 1.
Conclusions Despite these limitations, the dense investigation of the most important resilience factors and resilience processes and the multi-modal database generated in DynaM-OBS have the potential to serve as a foundation for future studies on AR is supported by Studienstiftung des deutschen Volkes. The funding agencies had no part in the study design; collection, management, analysis, and interpretation of data; writing of the report; and the decision to submit the manuscript for publication. The authors thank S Stöber and N Donner for their help with project administration. The authors thank the following people for their help with conducting the study: at Charité: A Dimitrov-Discher, V Wessel, P Eisenhauer, L Schmidt, E Hodapp, C Sachs, N Hoerner, and D Alkhoury; in Mainz: S Paleske, K Aschenbroich, L Dressler, C Sakas Gandullo, A Peschel, C Walter, M Weber, and J Piloth; in Nijmegen: J Asbreuk, E Hermsen, and Y Schwarze; in Tel Aviv: I David, G Shahaf, S Toledano, and L Andrawus; and in Warsaw: N Robak, M Górecki, J.
interventions and trainings aiming to enhance resilience factors and adaptive processes in at-risk individuals. mental health and psychosocial concerns resulting from the COVID-19 pandemic [RESPOND] project); Deutsche Forschungsgemeinschaft (DFG; grant Collaborative Research Centers [CRC] 1193, subprojects B01, C01, C04, and Z03); the German Federal Ministry for Education and Research (Bundesministerium für Bildung und Forschung [BMBF]) as part of the Network for University Medicine under grant 01KX2021 (CEOsys and EViPan projects); and the State of Rhineland-Palatinate, Germany (Mainz Resilience Project [MARP] program and Deutsches Resilienz Zentrum [DRZ] program, Leibniz Institute for Resilience Research).
We recorded conditions in which samples were collected and any associated environmental data (e. g. , temperature, DO). We identified 43 primary journal articles published between 1966 and 2020 (Table  S1 ), with most covering a localized spatial scale and with 49 and 33% conducted in North America and Europe, respectively (Fig. S1C ). Most studies were conducted in a cool, wet temperate climatic zone (46%), followed by Mediterranean (24%) and arid zones (24%). Two research gaps in particular reflected the disconnect between discipline-specific conceptualizations of the HZ: 1) few studies explicitly included sampling during the unsaturated, dry, or rewetting phases of the HZ; and 2) only 40% examined how physicochemistry (which, as discussed, varies with hydrology) affects biota during saturated and unsaturated phases.
ACKNOWLEDGEMENTS Author contributions: AGD coordinated manuscript contributions and led the writing. RS, MS, and MAZ assisted with section leadership. All authors contributed to the idea development and writing of the manuscript. This manuscript is a product of the  Dry Rivers Research Coordination Network , which was supported by funding from the  National Science Foundation  (NSF;  DEB 1754389 ). Additional support to AGD was provided by  NSF   DEB 1830178.
Recently, Gaia Data Release 3 (hereafter DR3, Gaia Collaboration 2022d) published the largest stellar catalogue with chemical abundances, atmospheric parameters, and radial velocities ever created. Radial Velocity Spectrometer (RVS) data were parameterised by the General Stellar Parameteriser -spectroscopy (GSP-Spec) module  (Recio-Blanco et al. 2022) , delivering chemo-physical parameters for 5. 6 million stars over the entire sky. Gaia Collaboration (2022c) observed azimuthal variations in red giant branch (RGB) stars, but did not present quantitative results or in-depth discussions on their origin. Thanks to the large number of chemical and astrometric measurements, as well as Gaia's all-sky sampling, it is now possible to map variations in the metallicity distribution of stars in the Galaxy as never before.
Data selection For this study, we used the main stellar atmospheric parameters (effective temperature T eff , surface gravity log(g), and global metallicity [M/H]) derived from Gaia RVS spectra by the GSP-Spec module to select tracers of the disc population and map large-scale chemical inhomogeneities in our Galaxy. The quality constraints on spectroscopic and astrometric measurements (as well as a Toomre kinematical selection) are detailed in Appendix A. To serve our purpose, we have focussed on giant stars, as they are intrinsically bright, and therefore they allow us to sample a relatively large volume of the Galactic disc. Also, taking advantage of the large number of high-quality chemical measurements for these stars 1  , a robust statistical analysis can be performed, thanks to high signal-to-noise spectra.