dataesr/scientific-paragraphs-categorization huggingface dataset:
The California homeowners earthquake insurance database developed for this study. Data quality scale is as follows. A (good): all data from scientific publications with methodology explained; (B) acceptable: at least one data point from a scientific publication without details on the methodology or one processing has been applied; (C) weak: data are uncertain. Financial amounts are in 2015 US dollars. The total written premium is given in millions of US dollars. Column names are Q: data quality; E: equation ID. Abbreviations are E 1 = {3; 6; 7; 10} and E 2 = {5; 6; 7}.
Acknowledgements. The authors acknowledge  Madeleine-Sophie Déroche ,  CAT Modelling R & D Leader  at  AXA Group Risk Management , for her contribution to this work. They are also grateful to the  California Department of Insurance , the  EM-DAT ,  Robert Shiller , the  US Census , the  US Department of Labor , and the  USGS  for providing open access to their databases. Financial support. This research has been supported by the  European Union 's  H2020 Research and Innovation programme  under the  Marie Skłodowska-Curie grant  (grant no. 813137 ) and  Labex   OSUG@2020 (Investissements d'avenir ,  ANR10-LABX56  grant). Review statement. This paper was edited by  Sven Fuchs  and reviewed by two anonymous referees.
Within-trial clustering was taken into consideration with the use of shared frailty models. The proportional-hazards assumption was examined with the use of the Schoenfeld residuals. As patients with diabetes treated with insulin tend to have a longer standing diabetes than those not on insulin, we compared the risk of each outcome in patients with diabetes on insulin with those not on insulin further adjusting for the duration of diabetes in addition to the confounders mentioned above. For patients with available echocardiographic data (745 patients in I-Preserve and 653 patients in TOPCAT, respectively), we compared the measurements of left ventricular structure and left ventricular systolic and diastolic properties according to diabetes status and insulin use at baseline. A two-sided P-value < 0.
33 -35 However, the misclassification of diabetes, if any, would weaken rather than strengthen effect estimates. Third, there was no differentiation between type 1 and type 2 diabetes. However, the overall findings did not change in the sensitivity analysis with the exclusion of patients having diabetes diagnosed under 20 years of age (4. 4%), who were presumably type 1 diabetes (online supplementary Tables S1-S5 and Figure  S1 ). Fourth, the TOPCAT Americas data have been published separately, although these account for only 21% of the total patients in the present data set in which we have conducted more comprehensive analyses. 15 Finally, data on the type and dose of insulin (and other glucose-lowering therapies), the level of glycaemic control (e. g. measurement of glycated haemoglobin), and burden of microvascular complications in patients with diabetes were not available.
Acknowledgements This work has received a French government support granted to the  CominLabs excellence laboratory  and managed by the  National Research Agency  in the " Investing for the Future" program  under reference  ANR-10-LABX-07-01. It was also financed by  Brittany region  under  HEMISFER  project, MRI data acquisition was supported by the  Neurinfo   MRI research facility  from the  University of Rennes I. Neurinfo  is granted by the  European Union (FEDER) ,the  French State , the  Brittany Council, Rennes.
Elemental ratios and anomalies REE data are presented as distribution patterns, with the individual REE listed in the order of their atomic number, normalized to the upper continental crust (thereafter called REE patterns). The change of the shape of the REE patterns between 2 successive reservoirs (i. e. in our study soil and plant) is called "fractionation of the REE", e. g. an enrichment or a depletion in light, or heavy REE. For that, (La/Sm) UCC, (Gd/Yb) UCC and (La/Yb) UCC ratios are calculated. More specific fractionations are positive or negative peaks in the REE distribution patterns, which are most common for Ce and Eu, and referred to as positive or negative Ce and Eu anomalies. The magnitude of these anomalies is quantified by Ce/Ce* and Eu/Eu* ratios: Ce/Ce* = (2 x Ce UCC )/(La UCC + Pr UCC ) (1) and Eu/Eu*=(2 x Eu UCC )/(Sm UCC + Gd UCC ) (2) The subscript "UCC" stands for normalized values.
Acknowledgments The authors thank  UniLaSalle, Beauvais, France , for funding BL  MSc  project. The  Belgian Fund for Scientific Research (FRS-FNRS ) is acknowledged for financial support to BL. Tenke Fungurume Mining s. a. r. l. permitted us the plants and soils collection. We are grateful to  Petru Jitaru  for his help in the ICP-MS analysis.
Aims: To elucidate differences between commonly-used mid-latitude geomagnetic indices and study quantitatively the differences in their responses to solar forcing as a function of Universal Time (UT), time-of-year (F), and solar-terrestrial activity level. To identify the strengths, weaknesses and applicability of each index and investigate ways to correct for any weaknesses without damaging their strengths. Methods: We model how the location of a geomagnetic observatory influences its sensitivity to solar forcing. This modelling for a single station can then be applied to indices that employ analytic algorithms to combine data from different stations and thereby we derive the patterns of response of the indices as a function of UT, F and activity level. The model allows for effects of solar zenith angle on ionospheric conductivity and of the station's proximity to the midnight-sector auroral oval: it employs coefficients that are derived iteratively by comparing data from the current aa index stations (Hartland and Canberra) to simultaneous values of the am index, constructed from chains of stations in both hemispheres.
The ap (and Kp) index is currently made from data from 11 stations in the northern hemisphere and 2 in the southern. It is available continuously since 1 January 1932, although the number and distribution of stations has varied considerably: initially there were 10 stations all in the northern hemisphere, the first southern hemisphere station being added in 1958. The ap and Kp indices have always been dominated by data from European stations. The aa index uses just two stations, one in each hemisphere, and although suppressing the resulting spurious annual variation in the response rather well, aa shows a large spurious diurnal variation (e. g. ,  Lockwood et al. , 2018b). The aa index is continuously available for the longest interval (1 January 1868 to the present day) and has been relatively homogeneous in its construction.
In theory, if the K to K S conversions were always ideal, the distribution of ap stations would be of no consequence as the various K S values would be all be different measurements of the same thing. However, to some extent the tables will fall short of the ideal so the index response pattern will also depend, to some extent, on the distribution of stations. Even if the K to K S conversions were ideal, converting the data to what Niemegk would have seen causes the index to take on the UT-F response characteristics of the Niemegk site. Note that we here quote ap (and hence ap C , Ap C * and [Ap C *] max ) in the most widely used formnamely as an index without units: the standard ap values are an index in units of 2 nT and hence the values in units of nT would be double those given in this paper  (Menvielle & Berthelier, 1991).
Black dots are means in 1-percentile bins of the mid-latitude range index along the x-axis (giving 614 samples in each bin) and the horizontal and vertical error bars are plus and minus one standard deviation for that bin. The mauve lines are fourth-order polynomial fits to the 3-hourly values. The RMS deviation of the observed 3-hourly SME max values from the fitted polynomial values, D RMS are given in each panel, as are the linear correlation coefficients r and r* for the 3-hourly values and the 8-point running means, respectively. Note that the polynomial fit order m = 4 that was used was the largest that could be employed (desirable as it preserves the non-linearity of the variation) that gave a good fit to the tail of the index distribution; the latter criterion being tested by evaluating the D RMS 2 / (nm À 1) value for the n = 614 samples in the top percentile of the index dataset and checking it was not significantly larger (at the 1 À r level) than the minimum value for any m.
44, whereas for ap it is a = 0. 48  (Lockwood et al. , 2019a). The correlation coefficients for the daily means Ap, Aa H and Am with daily means of P a /hP a i all are very high, being 0. 866, 0. 893 and 0. 923, respectively. The root-mean-square (RMS) fit residual for a linear regression of all the data, e, was also computed, giving e/hApi all = 0. 570 for Ap, e/hAai all = 0. 401 for Aa and e/hAmi all = 0. 342 for Am. Hence both these metrics give best agreement for am, and worst agreement for ap. We tested the significance of the difference between the correlations using the Meng-Z test (which allows for intercorrelations between the datasets  (Meng et al. , 1992) ) and found that the p-value for the null hypothesis that they are actually the same was undetectably small. Hence the agreement with the coupling function is significantly better for am than for aa and that for aa is significantly better than that for ap on this 1-day averaging timescale.
56, 47. 76, 37. 71 27. 50, 13. 96, and 5. 32  nT. Modelled sensitivities are computed at points 1 h apart in UT and 1/365 apart in F and then averaged into the same sized UT-F bins (3 h by 0. 05) as used for the observations. Note that the left-hand plots are colour-contoured using the 0. 8-1. 2 scale given by the lower colour bar while the modelled and observed s HAD /S am sensitivity ratios both use the 0. 5-1. 6 scale given by the upper colour bar. In all plots unity values are coloured yellow. M. Lockwood et al. : J. Space Weather Space Clim. 2019, 9, A20 am or ap index; that limit being lower for the indices, by virtue of the averaging of data from a number of stations. This means that there are times (as in the case of the bottom row of Fig. 8 ) when the station is not detecting any activity yet the am index is: in Figure  8  (for Canberra in the southern hemisphere) this is particularly true in mid-winter (F = 0.
5% in 1890 and À16. 7% in 2010 for low activity and À18. 9% in 1890 and The "checkerboard" response pattern for aa seen in Figure  10  is present for both low and high activity and is found in the aa data, as demonstrated by Figure  11. This plot shows the sensitivity of the aa index S aa = (haai/hami)S am , where the ratio haai/hami is taken from data for all available years. Note that the modelled sensitivity of the am index, S am , is very close to unity at all F and UT and so the patterns for haai/hami are almost identical to those shown. The left-hand and right-hand panels are for high and low geomagnetic activity respectively. To get enough samples for this plot, high activity is defined in Figure  11  as am ! 40 nT both when averaging the observed haai and hami values and when calculating the model sensitivity of am, S am.
A corrected ap index, ap C In this section, we present a method for empirically correcting the ap index to allow for its non-uniform response function. We do this using the same basic principle as introduced by Bartels and still used today to compile ap, namely activity-dependent look-up tables to correct the time-of-day/time-of-year response. A difference is that we are applying it to an index and not the data from a single station. The tables are provided in the form of arrays of values that can be interpolated to give the multiplicative correction factor required for a given ap, UT and F. We recommend use of Piecewise Cubic Hermite Interpolating Polynomial, (PCHIP) interpolation because it maintains smooth changes in gradient through the data points. Tests show that, although in general that it can sometimes give excessive oscillation between points, in this case it gives more accurate value than a linear interpolation).
This is valid for three-hourly values to within extrema of ±2. 9% at low geomagnetic activity, falling to just ±0. 5% at high geomagnetic activity. As an overall average, the modelled (UT-F) response pattern of am is constant to within a standard deviation of 0. 65% with extreme deviations of +1. 4% and À1. 2%. The ap (Kp) index station distribution is not uniform but the use of the K to Ks conversion tables makes allowance for this. These tables were constructed using data from a limited number of years when solar and geomagnetic activity were at moderate levels only. Hence it not that surprising that they may not be ideal over all the 1957-2017 interval tested here. The general similarity of the average annual and diurnal variations of ap and am in Figure  13  indicates that the tables are performing well in that they do not introduce major errors.
, 2018a, b)  performs considerably better than aa, having a flatter UT-F response at all activity levels. Naturally, being based on just one station in each hemisphere it cannot match the performance of am, but it has the advantage (for studies of long-term change or requiring large sample numbers) of extending back to 1868 whereas am extends back to only 1959. We have also presented a method for correcting the ap index for its uneven response pattern. The correction presented here uses all ap and am data since 1959, despite the fact that the network of stations used to generate the am index has undergone (relatively minor) changes in that interval and that used to generate the ap index has undergone considerable changes. This means that, in effect, we are accepting the tabular K-to-K S conversions inherent in ap for the purposes in deriving a correction factor to ap, f c (ap, F, UT), but this is one step removed from accepting them for the purposes of compiling ap itself.
Note that in the case of aa H , the correction is made through application of the station sensitivity model, whereas for ap C it is purely empirical. The most revealing division of activity levels is to consider the lowest 20% of samples (as determined by am), the highest 20% and the remainder in the inter 20-percentile range. The bottom two rows of Table  5  give the factor improvement brought about in the corrected indices: ideally these values should be infinite as am is used to correct the data and then used to test the corrected index. However, this is not going to be the case for a variety of reasons: in the case of aa H , the main reason is that by averaging many stations am will always suppress geophysical and instrumental noise more effectively than aa which is based on just two stations; in the case of ap C , the main reason is the effect of changes in the ap observation network.
Recommendations The major differences between the indices studied in this paper arise from the geographic distribution of stations used to compile them. The aa index uses just two stations in order to give a long data sequence. The am index has been designed to make the stations' distribution as even as possible and so give the most even time-of day/time-of-year response that is allowed by the availability of land that is suitable for housing a magnetometer. On the other hand, the ap index is, and has always been, dominated by European stations and is constructed in a way that re-calibrates all data to a European station (Niemegk): we have shown that because it is averaging more data that is similar (or is made similar), this gives it better sensitivity and more accurate annual means but an un-even time-of-day/timeof-year response.
On the other hand, the ap corrections (to give ap C ) are empirical and based on comparison with past am index data and so will become increasingly unreliable with time for future data. Hence ap C will be useful in re-evaluation of some past work for which evenness of the time-of-year/time-ofday response to solar wind forcing is important (for example studies involving the ranking order of the largest geomagnetic storms,  Lockwood et al. , 2019b, c)  but it would not make sense to recommend its use for future work with that requirement, because as long as the am index is available it is the better option in this context. That is not to say, that the ap index is not of value in other contexts. For example, in annual means of the data, the time-of-year/time-of-day response is averaged out and so long term trends are well monitored and for such Table  5.
Standard deviations and maximum and minimum percentage deviations from unity of observed 3-hourly index sensitivities, S, estimated assuming the am index is ideal for 1959-2017, for low, middle and high geomagnetic activity levels. The bottom two rows give the factor by which the corrected index is improved in terms of the uniformity of its response. applications keeping the same index (and keeping its compilation as homogeneous as possible) is important. We note in that ap extends back 27 years further into the past than am and so for studies where the sub-annual response is important, ap C can be used to extend the am data sequence back a further 27 years to 1932. Looking to any potential development of the networks of stations, it is important that any changes made enhance, rather than undermine, the strengths of the index in question.
Acknowledgements. The authors are grateful to the staff of The ISGI, France and collaborating institutes for the compilation and databasing of the am index which were downloaded from http://isgi. unistra. fr/data_download. php. We also thank the staff of  Geoscience Australia ,  Canberra  for the southern hemisphere aa-station K-index data, and at  British Geological Survey (BGS), Edinburgh  for the northern hemisphere aa-station K-index data. For the SuperMAG indices data we gratefully acknowledge: Intermagnet;  USGS ,  Jeffrey J. Love ; CARISMA, PI Ian Mann; CANMOS; The S-RAMP Database,  PI K. Yumoto  and  Dr. K. Shiokawa ; The SPIDR database; AARI, PI Oleg Troshichev; The  MACCS program ,  PI M. Engebretson ,  Geomagnetism Unit of the Geological Survey of Canada ; GIMA; MEASURE,  UCLA IGPP  and  Florida Institute of Technology ; SAMBA, PI Eftyhia Zesta; 210 Chain, PI K.
, 2013). Datasets suitable for evaluating shifts in marine phenology exist but are often difficult to access and remain underutilized  (Thomas, Fornwall, Weltzin, & Griffis, 2014). Where evaluations of marine phenology have been explored, rates of advancement are faster than in terrestrial systems  (Poloczanska et al. , 2013). Rapid and differential shifts among dependent species (e. g. , predator-prey) and across trophic levels increase the potential for asynchronies and mismatches in food and other resources, thereby leading to negative impacts on individual fitness, population dynamics, and ecosystem function  (Doney et al. , 2012; Durant et al. , 2013; Staudinger et al. , 2013). In addition, mismatches with human uses and management tools may occur, resulting in disruptions to fisheries and other ecosystem services, thus impeding resource use, conservation, and management  (Mills et al.
, 2016). While study design and data collection methodology are clearly important for capturing phenological events, the advancement of this field often requires using long-term historical datasets that were not collected with the same methods or with phenology in mind. Therefore, choice of phenology metric, preprocessing (e. g. , combining time series, data smoothing, filtering), and analytical techniques also play important roles in overcoming data deficiencies as well as isolating and interpreting signal from noise across spatiotemporal scales  (Ferreira et al. , 2014).
, 2017) , and consideration of multiple drivers are needed  (Jonsson & Jonsson, 2009). Such integrative studies are becoming possible through collaborative partnerships and big data networks such as the Northeastern Regional Association of Coastal Ocean Observing Systems (NERACOOS: www. neracoos. org/), as well as citizen science and volunteer monitoring programs. These efforts seek to increase the availability of an array of high-quality environmental and ecological datasets for broad-scale climate-relevant analyses but need to be updated regularly and have sufficient coverage to support phenological studies. Examples of long-term monitoring programs in the GoM that if properly maintained could be used to further explore phenology-related questions are compiled in Supporting Information Appendix S4.
Coordinated regional monitoring that builds on historical datasets of community composition and occurrence, migration timing, and life stage-specific information across broad temporal periods and seasonal habitats is needed to test theories of how changes in phenology affect species interactions  (Staudinger et al. , 2013). However, some long-term monitoring programs are being scaled back (e. g. , Continuous Plankton Recorder Survey) at a time when expanded effort is needed to capture ecosystem-level changes. Surveys that are fixed in time (i. e. , effort begins and ends on predetermined days of year) are particularly susceptible to inadvertently missing shifts in phenological events. It has been hypothesized that species that have historically migrated in and out of the region might become year-round residents if conditions change substantially  (Ramp et al.
| FUTURE RE S E ARCH AVEN UE S FOR PHENOLOGY IN THE G ULF OF MAINE Key gaps in knowledge revealed through this synthesis can guide new research initiatives to reduce uncertainty and help regional managers prepare for increasingly variable conditions in the GoM. In many cases, strong anecdotal information exists pertaining to possible changes in phenology for valued and potentially vulnerable functional groups, but rigorous phenology-focused analyses of existing data sets have yet to be performed. Where possible, we highlight potential long-term survey and monitoring datasets that may be useful for evaluating phenological responses and ecological disruptions. However, substantial effort may be needed to digitize, compile, and/ or organize these existing resources into formats conducive for addressing questions at the appropriate spatiotemporal scale.
| Seabirds Long-term phenology records exist for a number of breeding sea- (https://usanpn. org/). Through these platforms, amateur and professional birders can contribute a diversity of observations with precise timing and location information as trip reports (checklists) that are stored in a central database, from which data can be queried and analyzed to evaluate specific questions. To date, application of these online databases has focused on studies of terrestrial songbirds and comparatively few seabird species. Substantial occurrence data exist for marine species in some areas of the GoM, particularly within eBird, and when paired with overlapping environmental data (e. g. , satellite data), has the potential to evaluate shifts and asynchronies in phenology (e. g. ,  Mayor et al. , 2017).
| Marine mammals and other large pelagics To the best of our knowledge, no studies published to date have documented phenological shifts in marine mammals in the GoM. However, just to the northeast in the Gulf of St. Lawrence,  Ramp et al. (2015)  documented advancements in arrival, departure, and residence times by humpback and fin whales over a 27-year period. Occurrences of these and other whales, as well as large pelagic animals such as seals, basking sharks, bluefin tuna, and sea turtles, have been monitored since the early 1980s using systematic aerial and shipboard marine mammal surveys  (Brown, Kraus, Slay, & Garrison, 2007; CeTAP, 1982). While survey effort has been variable, many of these datasets could support the development of occupancy and habitat suitability models to evaluate and verify anecdotal observations of occurrences in increasingly uncharacteristic locations in the GoM at anomalous times of the year (e.