dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements We are grateful to our frontline field staff for providing essential and life-saving services to vulnerable populations, despite the enormous challenges posed by the contexts in which they work. We would like to acknowledge their commitment to their patients and the increadible work that they carry out on a daily basis to decrease the inequalities in child health globally. Contributors SJ: substantial contributions to the conception, drafting of manuscript, revision, final approval and submission. NR, NM, DM, MT, NL, OO, ED, RP: substantial contributions to the conception, revision and final approval. HR, IC, AG: substantial contributions to the conception and final approval. Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
One of the World Climate Research Programme Grand Challenges is to evaluate whether existing observations are enough to underpin the assessment of weather and climate extremes. In this study, we focus on extreme associated with atmospheric rivers (ARs). ARs are characterized by intense moisture transport usually from the tropics to the extra-tropics. They can either be beneficial, providing critical water supply, or hazardous, when excessive precipitation accumulation leads to floods. Here, we examine the uncertainty in gridded precipitation products included in the Frequent Rainfall Observations on GridS (FROGS) database during two atmospheric river events in distinct Mediterranean climates: one in California, USA, and another in Portugal. FROGS is composed of gridded daily-precipitation products on a common 1 • × 1 • grid to facilitate intercomparison and assessment exercises.
The database includes satellite, ground-based and reanalysis (RE) products. Results show that the precipitation products based on satellite data, individually or combined with other products, perform least well in capturing daily precipitation totals over land during both cases studied here. The RE and the gauge-based products show the best agreement with local ground stations. As expected, there is an overall underestimation of precipitation by the different products. For the Portuguese AR, the multi-product ensembles reveal mean absolute percentage errors between -25% and -60%. For the western US case, the range is from -60% to -100%.
The variability of weather extremes across different temporal and spatial scales is one area ripe for significant advances; however, one needs to account for uncertainties not only in model simulations  (Knutti and Sedláček 2013 , Soares et al 2017 , Cardoso et al 2019)  but also in observations (e. g. Lockhoff et al 2014 , Herold et al 2017 , Hénin et al 2018 , Herrera et al 2019 , Kotlarski et al 2019). As a result, the observation-based precipitation community is engaged in a broad scope assessment  (Haddad and Roca 2017)  with a dedicated focus on extreme precipitation  (Alexander et al 2018). In support of these objectives the Frequent Rainfall Observations on GridS (FROGS) database (Roca et al 2019) has been developed. It is a unique repository of various 1 • × 1 • gridded daily products originating from in situ, RE and satellite products.
Considering the importance of uncertainty assessment in the climate research community, a collaborative project entitled the Atmospheric River Tracking Method Intercomparison Project (ART-MIP) is currently ongoing. The goal of ARTMIP is to understand and quantify uncertainties in AR research based on the choice of the detection/tracking methodology  (Shields et al 2018). The climatological characteristics of ARs, such as frequency, duration, intensity, and seasonality are all strongly dependent on the method used to identify ARs  (Ralph et al 2018a , Rutz et al 2019). In the present study, a contribution to better understand the uncertainties related to AR impacts is attempted by quantifying the extreme precipitation related to two AR landfall cases in two Mediterranean climates using a large set of precipitation products from the FROGS database.
Precipitation datasets and methodology The current study focuses on two Mediterranean climates, Portugal and the western U. S. , where strong precipitation has been shown to be highly connected to AR landfall interacting with complex terrain (e. g. Neiman et al 2013 , Ramos et al 2015). In these cases, local ground stations are the best reference data if there are an appropriate spatial and temporal distributions. Taking this into account, to ensure the maximum number of local ground stations (especially in the Portuguese domain), we choose two case studies in 2016. Using in situ reference datasets avoid some of the caveats linked with regular observational gridded products, such as temporal inhomogeneities due to a changing station network and the smoothing of extremes due to interpolation methods (e.
g. Belo-Pereira et al 2011 , Herrera et al 2012 , 2019). In fact, observational uncertainty in gridded precipitation datasets is substantial and comparable to that linked to climate models  (Kotlarski et al 2019). Here, in the case of Portugal the ground-station data used is from the Portuguese Institute of Meteorology while for the western United States we use the Global Historical Climatology Network-Daily (GHCN-Daily) database  (Menne et al 2012a). More information regarding each dataset can be seen in sections 2. 1 and 2. 2.
Portuguese station data The precipitation for Portugal was provided by the Portuguese Institute of Meteorology (Instituto Português do Mar e da Atmosfera, IPMA), which provides accumulated rainfall reported every 10 min from 72 automatic weather stations. These stations were chosen based on a combination of tests for temporal completeness over the period of interest in this case (see section 3) quality (Santo et al 2014), and their spatial distribution over mainland Portugal. The daily precipitation was accumulated between 0000 UTC and 2359 UTC.
Western United States station data The precipitation dataset used to evaluate the AR that impacted the western U. S. is the GHCN-Daily database  (Menne et al 2012a). Observations in this database are integrated from around 30 different data sources (about a dozen within the U. S. ). Updates occur 7 d a week, and the entire database is reconstructed approximately weekly, due to the growing list of networks that contribute to it. During this reconstruction, a consistent suite of over 20 different quality checks are applied to the data. Each version of this reconstructed dataset is archived for future retrieval. Generally, data in the U. S. are finalized 45-60 d after the end of the month, indicating that the date of access for this study's look at the October 2016 event means data values should be unchanging.
Many more details on this database can be found in an overview published by  Menne et al (2012b). Some of the original information from source datasets, such as original quality flags, is not provided in this database. However, even if they were provided, the contributing networks have extremely diverse quality control procedures  (Jaffrés 2019). GHCN-Daily attempts to provide a dataset with clear quality checks that work consistently across many different networks. Thus, we consider it to be the most relevant, and comprehensive option for assessing precipitation during the event discussed here. In order to be conservative, all data with any quality flag in the GHCN-D dataset are excluded from this analysis.
The FROGS dataset Twenty-two daily precipitation estimates originating from multiple sources, including in situ, atmospheric RE, and satellite-based products are analyzed in this study. Each individual dataset has been gridded to the same 1 • × 1 • regular longitude-latitude grid as part of the FROGS database (Roca et al 2019) effort. More information on the FROGS initiative can be found at http://frogs. ipsl. fr. Table  1  summarizes the various products used. Following previous investigations (e. g. Alexander et al 2020), the products have been clustered into four groups according to the data type to facilitate the assessment. The groups are in situ gauges only (GO) and RE. The satellite products have been further split in satellite only (SO) products and satellite-withgauges (SG) products.
The bias offers a view of the overall deviation between model and products values, and together with MAE allows the identification of systematic errors. The percentage error measure, MAPE, gives a relative measure of those errors with reference to the mean observed values. Finally, the RMSE, due to the root mean, emphasizes the larger deviations between observation and FROGS data. For the ensemble of product types (RE, SO, SG and GO) the same strategy is applied, which is pooling together all the data for each grid box and not applying the mean. Additionally, for comparison purposes, we computed the average of the observational ('av' from now forward) values at 1 • grid-point (the same as grid box of the FROGS dataset). Afterwards this mean observational value is directly compared with the FROGS regular gridded products values and averaged to get the mean error of each product.
Discussion and summary A comparison between the new FROGS daily precipitation dataset, which includes mostly satellite derived products, and local in situ weather stations is made for two mid-latitude regions (western US and Portugal) for two specific AR landfall case studies. It was shown by different authors that at daily scale satellite gridded products exhibit significant skills in the tropics  (Roca et al 2010 , Gosset et al 2018). However, at a daily scale, the skill of extreme precipitation derived from satellite and RE at mid-latitudes is not well known. Lockhoff et al (2019)  used a limited number of datasets and a systematic multi seasonal assessment to reveal the complexity of the products' behaviour with skills depending upon seasons and the underlying climatological regimes. Our results complete the global picture by focusing on ARs and confirm the general outcome of the previous analysis.
The two case studies were selected considering the observation of IWV from satellite data and SLP from different RE to meet the criteria of the ARs definition (AMS Glossary, https://glossary. ametsoc. org/wiki/Atmospheric_river). In addition, the authors would like to stress that both cases had socio-economic impacts corresponding and with extreme precipitation values. Even though there are only two selected case studies in this work, it is the first time that the FROGs database is used to study extreme precipitation within ARs. We acknowledge the fact the conclusions can't be extrapolated to other ARs that impacted the selected regions. This could be done using a large ARs dataset but keeping in mind that each ARs in unique and have different categories based on intensity and persistent as shown in  Ralph et al (2019).
Therefore, further analysis using a large set of ARs should be separated into categories to allow a fair comparison between the different ARs precipitation measured by the rain gauges and the FROGs dataset. Two different studies over the western United States quantified AR-driven precipitation using different satellite products at a sub-daily scale  (Behrangi et al 2016 , Wen et al 2018). These studies show that the satellite products usually underestimate the heavy precipitation compared to gauge measurement, while some are able to capture the orographic enhancement over the California mountains. In addition, for extremely heavy precipitation (3-hourly precipitation rate >5 mm h -1 ), none of the products show good performance in quantifying the precipitation intensity  (Wen et al 2018). Regarding the Iberian Peninsula, as far as we know, no specific comparison has been made between satellite precipitation products during an AR event.
Hénin et al (2018) , shows that the Tropical Rainfall Measuring Mission (TRMM) product overestimates (underestimates) daily precipitation sums for the least (most) extreme events over the Iberian Peninsula. When analyzing these two case studies of ARs affecting two different Mediterranean climates, amongst the FROGS database, the products based only on satellite data or combined with it perform the poorest in capturing the daily precipitation over land in the western US and Portugal. The RE and the gauge-based products possess the best agreement with local ground stations. As expected, there is an overall underestimation of precipitation by the different products. For Portugal, MAPEs reach values between ∼60% and 70%, and for western US values from 60% to 100%. Those larger MAPE values correspond to the SO product.
New York Acad. Sci. 1436 217-30 Ramos A M, Trigo R M, Liberato M L R and Tomé R 2015 Daily precipitation extreme events in the Iberian Peninsula and its association with atmospheric rivers J. Hydrometeorol. 16 579-97 Roca R, Alexander L V, Potter G, Bador M, Jucá R, Contractor S, Bosilovich M G and Cloché S 2019 FROGS: a daily 1 • × 1 • gridded precipitation database of rain gauge, satellite and reanalysis products Earth Syst. Sci. Data 11 1017-35 Roca R, Chambon P, Jobard I, Kirstetter P, Gosset M and Bergès J Claude 2010 Comparing satellite and surface rainfall products over West Africa at meteorologically relevant scales during the AMMA campaign using error estimates J. Appl. Meteorol. Climatol. 49 715-31 Rutz J J et al 2019 The Atmospheric River Tracking Method Intercomparison Project (ARTMIP): quantifying uncertainties in atmospheric river climatology J.
Acknowledgments The financial support this work was possible through the  Fundação para a Ciência e a Tecnologia (FCT)  project:  HOLMODRIVE-North Atlantic Atmospheric Patterns Influence on Western Iberia Climate: From the Late Glacial  to the Present ( PTDC/CTA-GEO/29029/2017 ). A M Ramos was supported by the  Scientific Employment Stimulus 2017 from Fundação para a Ciência e a Tecnologia (FCT ,  CEECIND/00027/2017 ). We would like to thank the three reviewers for their thoughtful comments and efforts towards improving the manuscript.
2a ). However, the position and number of bands is different for the hairs of opossums and ying squirrels, indicating the presence of other porphyrins. Following ref. 32  's database, the position of Q-bands at 501. 4, 535. 4, 570. 5 and 619. 6 nm in the opossums' hairs (Fig. 2a ) is consistent with uroporphyrin I or III, and to a lesser extent with porphyrin c. The fth band observed at 638. 4 nm most likely indicates that the uroporphyrin is protonated, as also suggested by the shoulder on the Soret band, two modi cations illustrated by our protonated protoporphyrin IX (PPIX2H) reference spectrum. For the ying rodents' hairs, the position of Q-bands at ~ 506, ~550, 578 and 640-646 nm only matches that of porphyrin S-411  32  , an analogue of coproporphyrin  33. As part of the heme biosynthesis pathway, porphyrins are associated with all aerobic and anaerobic metabolisms and are ubiquitous in the cells of many organisms  26, 34.
Acknowledgments We thank the  Museum für Naturkunde of Berlin , especially  C. Funk ,  A. Rosemann ,  F. Mayer  and  D. Willborn ; and the  Naturkundemuseum of Potsdam , especially  I. Pokorny , for lending us various specimens of mammals (Germany). We are grateful to  B. de Thoisy  and  A. Herrel  for lending us fresh marsupial specimens from the  JAGUARS collection (French Guyana). We thank the  Dortmund Zoo , especially  M. Stawinoga  and  I. Schappert  for their support with data acquisition. Finally, we thank  M. Jansen  for helpful advice in data interpretation. E. A. was supported by the  German Research Council (Deutsche Forschungsgemeinschaft ; grant number  AM 517/1-1 ). J. P. was supported by the  Elsa-Neumann-Stipendium (Humboldt-Universität zu Berlin ). S. L. D. T. was supported by the  Fyssen Foundation  and the  Alexander Von Humboldt Foundation.
Fig. 1. In contrast, treatment of 1 with lithium and sodium alkyls does not furnish 4Li and 4Na, and instead 2Li and 2Na are consistently isolated. To complete and provide a calibrated 5M series, we treated dimeric 4Li and 4Na, prepared by an established procedure  18  , with two equivalents of Me 6 -Tren to produce monomeric 5Li and 5Na. The NMR and IR spectra of 3M and 5M are consistent with their formulations (Supplementary Figs. 1-25). Crystallographic characterisation. The characterisation data and crystallographic unit cell checks for 4K/Rb/Cs produced by disproportionation match those of authentic samples  [18] [19] [20]. The solid-state structures of the 3M and 5M series were determined, confirming their formulations, Figs. 2  and 3  (Supplementary Tables  1 2 3 4 5 6 ). For each complex, a triamidoamine-uranium component is bound to a central nitrogen, that resides in the pocket defined by the Tren TIPS ligand, with that nitrogen atom bridging to a Me 6 -Tren-capped alkali metal.
ATR-IR spectra were recorded on a Bruker Alpha spectrometer with a Platinum-ATR module in the glovebox. UV-Vis-NIR spectra were recorded on a Perkin Elmer LMD TM 750 spectrometer. Data were collected in a 1 mm path-length cuvette loaded in an MBraun glovebox and were run versus the appropriate solvent. EPR spectra were measured using a X-band (9. 44 GHz) Bruker Elexsys E500 spectrometer equipped with an ER4118SMS5 resonator at 5 K. Solution samples were transferred to the quartz tubes under argon, and the tubes were flame sealed prior to experiments. Field corrections were applied to the raw data using Bruker strong pitch (g = 2. 0026) as a reference. Static variable-temperature magnetic moment data were recorded in an applied dc field of 0. 5 T on by Quantum Design MPMS XL7 or MPMS 3 (for 3Li and 5Na) SQUID magnetometers using recrystallised powdered samples.
Care was taken to ensure complete thermalisation of the sample before each data point was measured and samples were immobilised in an eicosane matrix to prevent sample reorientation during measurements. Diamagnetic corrections were applied for using tabulated Pascal constants and measurements were corrected for the effect of the blank sample holders (flame sealed Wilmad NMR tube and straw) and eicosane matrix. CHN microanalyses were carried out by Martin Jennings and Anne Davies at the University of Manchester. Preparation of [U(Tren TIPS )(μ-NH)(μ-Li)(Me 6 -Tren)] (3Li). Method A: A solution of Me 6 -Tren (0. 17 g, 0. 75 mmol) in hexanes (5 ml) was added slowly to a pre-cooled (-78 °C) solution of Bu t Li (0. 035 g, 0. 55 mmol) in hexanes (10 ml). After 10 min, a solution of 1 (0. 43 g, 0.
Acknowledgements We gratefully acknowledge funding and support from the  UK Engineering and Physical Sciences Research Council  (grants  EP/K024000/1 ,  EP/M027015/1 ,  EP/P001386/1 ,  EP/ S033181/1 ),  European Research Council  (grant  CoG612724 ),  Royal Society  (grant  UF110005 ),  Leverhulme Trust  ( RF-2018-545\4 ),  Humboldt Foundation ,  CalMip ,  The University of Manchester ,  COST Action   CM1006 , and the  UK National Electron Paramagnetic Resonance Facility.
Data availability The X-ray crystallographic data for 3M and 5M reported in this study have been deposited at the Cambridge Crystallographic Data Centre under accession codes 1997349-1997358. These data can be obtained free of charge from The Cambridge Crystallographic Data Centre (www. ccdc. cam. ac. uk/data_request/cif). All the other data supporting the findings of this study are available within the article, its Supplementary Information (Supplementary Figs. 1-45  and Tables  1-47 ), or from the corresponding authors upon reasonable request. Source data are available from the authors upon reasonable request.
Students collect metadata, often on a paper datasheet, which is later entered into the "All About Arsenic" project on the citizen science data portal Anecdata. org. The metadata include the collector's name, the student name, the location of the well, the type of well, whether the water was filtered and whether the filter was for the whole home or located at the tap. Information about previous testing is also collected. The name and address of the sampler and exact well location are not available in the publicly available dataset on Anecdata. However, disclosure forms are provided to families so that they can give permission for data to be shared with the Maine Center for Disease Control, New Hampshire Department of Environmental Services, and with scientists conducting research on the well water samples.
Behavioral studies. Zebrafish larvae were analyzed at 5 dpf for mortality, chorion presence, and any obvious deformities. For a given sample treatment, 8 larvae were randomly selected and placed individually into wells containing 2 mL of egg water each in a 24 well plate. This process was repeated 3 times, yielding behavior data for 24 larvae. There were certain instances where exposure to well water resulted in significant mortality or hatching inhibition. Larvae that remained in their chorion were manually dechorinated and allowed to equilibrate before being plated in 24 well plates, larvae that exhibited minimal movement post-dechorination were not used. In groups with high mortality, extra embryos from the same exposure group were used to obtain a sufficient number of larvae for the behavior analysis.
The mortality and hatching data are indicated in Fig. 2  and Supplemental Table  SI. For the behavior analysis, plates containing larvae were kept in a water bath inside Danio Vision (Noldus, Leesburg VA) to maintain temperature at 28. 5 °C throughout the run. The Ethovision (Noldus, Leesburg, VA), a software used in tandem with Danio Vision, was used for a light/dark test, which included a 5:00 min dark habituation period, 5:00 min light period, 5:00 min dark period, 5:00 min light period, and 5:00 min dark period (for a total of 25 min). After the run was completed, larvae were removed and euthanized with MS-222. Zebrafish research were carried out in accordance with ARRIVE guidelines and all methods were in compliant with relevant guidelines and regulations and were approved by the University of Maine IACUC committee, proposal number A2017-05-04.
Statistical analysis. The distance traveled by each larvae was extrapolated from Ethovision software at 30 frames/second as mm moved per minute and summed over 25 min to determine the total distance (TD) traveled by a given larvae during the experiment. Averages of TD over 25 min from each larvae were calculated for each treatment group. Prior to statistical analysis, TD data were checked for normal distribution (Shapiro-Wilk), homogeneity of variance (Levenes test), and outliers in SPSS software (IBM, Armonk, NY). To test for statistical significance between TD across sample treatment groups, a one-way analysis of variance (ANOVA) and Tukey post-hoc was conducted using GraphPad Software (Prism, San Diego, CA). This test designated control-like, hyperactive, and hypoactive behavioral groups.
3 , Supplemental Fig. SII ). Given previous behavior toxicity studies, we focused on examining a correlation between As, Cd, Pb, and U concentration and larval behavior (Supplemental Fig. SIII ). To evaluate metal mixture effects, we conducted a principal component analysis (PCA) using SPSS software (see supplemental methods for syntax, IBM, Armonk, NY). The PCA generates 2 principal components (PC1, PC2) for each of the 92 well water samples based on their chemical composition, which was inserted into the PCA as raw concentration values in μg/L. The PCA can be performed based on all 12 chemical composition data or user picked combination of chemicals (e. g. , PCA can be conducted based only on As, Cd, and Pb concentration data to determine principal components for all 92 wells). Given there are 12 metals, numerous combinations of metal concentration data, can be integrated into the PCA.