dataesr/scientific-paragraphs-categorization huggingface dataset:
For instance, conservation management may equate to monitoring and surveillance (when the present conditions and ongoing and projected pressures do not require active management), establishing legal protection, establishing other area-based conservation measures such as community-managed forests  26  , or incentives such as payment for ecosystem services. We make no assumptions about the type of local management required, its feasibility and costs, or the counterfactual outcome with an alternative form of management, and therefore our prioritization is based on the upper limit of the value of these areas for achieving global conservation targets. We obtained fine-scale range maps for the world's terrestrial vertebrates as well as the largest sample of vascular plant range data ever considered in global species-level analysis, comprising ~41% of all accepted plant species names.
We tested the implication of prioritising the improvement of conservation status for vertebrate species by weighting them by current IUCN Red List threat category or evolutionary distinctiveness. We found that doing so has only small inefficiency implications compared to prioritisation without these weights, with 0. 7% fewer species conservation targets achieved when prioritising threatened vertebrate species and 1. 7% fewer when prioritising evolutionarily distinct vertebrate species under a 10% of land area budget. Yet, overall spatial patterns of the top-ranked 10% of land areas of conservation importance were comparable, with only minor differences, notably highlighting the importance of New Zealand and the Brazilian Amazon for conserving threatened vertebrate species, the Mediterranean Basin, north-western USA, Florida and fringes of the Amazon Basin for conserving evolutionarily distinct vertebrate species (Extended Data Fig.
Biodiversity data We used the best available global species range data (overview in Supplementary Table  1 ), including all extant terrestrial vertebrates and a representative proportion (~41%) of all accepted species in the World Checklist of Vascular Plants  42. Extant mammal (5,685 species) and amphibian (6,660) species range data were obtained from the International Union for Conservation of Nature Red List database (IUCN ver. 2019-2  43  ), while bird (10,953) range maps were obtained from Birdlife International  44. Data on the reptile ranges were obtained from the IUCN database when available (6,830 species), otherwise from the Global Assessment of Reptile Distributions (GARD) database (3,755  45  ). We obtained native plant range maps (193,954 species) from a variety of sources, including IUCN, Botanic Gardens Conservation International (BGCI) and the Botanical Information and Ecology Network (BIEN).
The IUCN and BGCI data contains expert-based range maps and alpha-hulls (see Supplementary Information), while the BIEN data consists mainly of herbarium collections, ecological plots and surveys  32, [46] [47] [48] [49] [50] [51] [52] [53]  , that we used to construct conservative estimates of species ranges using species distribution models (SDMs). We used version 4. 1 of BIEN, which includes data from RAINBIO  54  , TEAM  55  , The Royal Botanic Garden and Domain Trust Sydney, Australia, and NeoTropTree  56. Additional plant plot data from a number of networks and datasets have been included in BIEN and a full listing of the herbaria data used can be found in the extended acknowledgements and online (http://bien. nceas. ucsb. edu/bien/data-contributors/all/). In cases where multiple data sources were available for the same plant species, we preferentially used expert-based range maps to characterize a species' spatial distribution.
Suitable habitat refinement Where data on species habitat and elevational preferences were available, we refined each species' range to obtain the area of habitat (AOH) in which the species could potentially persist  58, 59. Data on species habitat preferences and suitable elevational range were obtained from the IUCN Red List database  43  and, for an additional 1,452 reptile species in the GARD database, habitat preferences were compiled from an extensive literature search. For seasonally migrating birds and mammal species we ensured that separate habitat refinements were conducted for permanent and seasonally occupied areas of their range, that is, the breeding and non-breeding range. Whenever habitat or elevation preferences were not available for a given species, we used the full range, excluding only areas considered to be artificial habitat type classes, such as arable or pasture land, plantations and built-up areas.
We note that this could exclude areas suitable for some generalist species. We do acknowledge that taxonomic biases can exist in the information on habitat preferences for some species which can be a limitation to our approach. For the AOH refinement we used a newly-developed global map (see Supplementary Information) that follows the IUCN habitat classification system, thereby avoiding crosswalks between habitat preferences and land cover maps  60. This data product integrates the best available land cover and climate data, while also using newly developed land-use data such as data on global forest management  61. Finally, per species range, we calculated the proportional amount (> 0-100%) of suitable habitat in each grid cell to include in the prioritisation analysis. Development of the habitat type map and all AOH refinement was performed in Google Earth Engine  62.
This proportion of species varied from 11% in islands of the North Pacific up to 100% in the Russian far east (mean 60. 1% ∓ 24. 5 SD). For 44 of these 50 regions we had range data for >40% of described plants in those regions. Having identified 10% as a minimum common denominator of completeness across most regions, we then used an iterative heuristic algorithm to identify 'representative' sets of plant species. This was done by (1) identifying the number of species that approximate a 10% threshold per WGSRPD level 2 region, (2) calculating random samples that approximated these 10% of species from each WGSRPD level 2 region, and (3) accounting for the fact that some species occur across multiple regions, resampling species at random if necessary. To test if this approach yielded sets representative of biogeographic patterns of the full dataset, we compared the spatial patterns of scaled vertebrate species richness to the 10% sets of these species for each WGSRPD level 2 regions, random subsets of 10% of all vertebrates and for all vertebrates combined.
We performed the test on vertebrates because we had range maps for ~95% of terrestrial vertebrates described, therefore allowing us to assess if our subsampling to representative sets can replicate "true" patterns in species richness obtained with a complete sample of species in a taxonomic group. Compared to a full vertebrate dataset, spatial patterns of scaled species richness were approximately identical across those sets (WGSRPD: Kendall  = 0. 954; Random: Kendall  = 0. 957), suggesting that this sampling approach can account for incomplete spatial coverage (Extended Data Fig. 5a ). We also checked if the frequency distribution of range sizes within our subsets matched the range size distribution of the entire set, using mammals as a test group, and found very modest differences between the full set and multiple subsets (Extended Data Fig.
Water data For capturing water quality regulation, we used estimates of potential clean water provision calculated by WaterWorld  76  and Co$ting Nature  77. The WaterWorld model uses a long-term climatology (1970-2015), long-term average Leaf Area Index (LAI) over the same period and the Copernicus land cover map  68  as baseline to be consistent with other assets in this analysis. We calculated for each grid cell the volume of water available, as the accumulated water balance from upstream based on rainfall, fog and snowmelt sources minus actual evapotranspiration. Second, clean water was assessed using the Human Footprint on Water Quality (HFWQ) index, which is a measure of the extent to which water runoff is from contaminating human land uses and diluted by passing through Natural ecosystems.
g. targets accumulation curves, we report the results across all representative sets. When representing spatial patterns of priorities, to take advantage of the full plant dataset, and to analyse only one global rank for each problem variant as opposed to 10, we combined the 10 nested sets for any given problem variant, (one for each of the 10 representative sets) into one single rank. This was calculated as the arithmetic mean ( 1 n ∑ i=1 n p i ) of the ten representative sets and ten budgets (n = 100), each with the proportion p of the grid cells that are part of a solution, which are then ranked and binned. Constructing a grand total average across different problem formulations does not formally constitute an optimal solution, because there are 100 different objective functions (one per budget and representative set) and these cannot be jointly optimized and synthesized further than 10 optimal nested sets, as we did, without resorting to heuristic solutions.
Our heuristic combination of optimal nested sets has the following characteristics: (a) it closely resembles spatial patterns and accumulation curves of individual nest sets, (b) allows us to make full use of the plant database while accounting for spatial biases in data availability, (c) allows for an estimation of the uncertainty in priority ranks due to the choice of alternative representative sets (Extended Data Fig. 1 ). All maps, unless otherwise noted, were the result of aggregating the nested sets of priorities for problem formulations with both carbon and water shortfall weighted as w = S + 1, where S is the set of biodiversity features in the analysis. This equal weighting was chosen as it was the one which yielded the lowest combined shortfall across features, and the most similar shortfall across all features (Fig.
Acknowledgements This work was conducted by the  Nature Map consortium. We thank  Richard Corlett & Tom Brooks  who provided feedback on an earlier version of the manuscript. We furthermore thank  Tom Hengl  ( OpenLandMap ) for his advice on the Soil Organic Carbon analysis. This study has benefited from a number of data providers and networks. We explicitly acknowledge all data providers in a separate Extended Acknowledgements owing to their length (Supplementary Information). The  Nature Map  project acknowledges funding from  Norway's International Climate and Forest Initiative (NICFI). Collection of the plant data used in this analysis has benefited from funding in form of a GEF grant  5810-SPARC  ' Spatial Planning for Area Conservation in Response to Climate Change '. CM acknowledges funding from  NSF  ( National Science Foundation ) grant  DBI 1913673.
RVG was supported by  Australian Research Council   DECRA Fellowship  ( DE170100208 ). Furthermore, EAN and XF were funded by the  Bridging Biodiversity and Conservation Science Program of the University of Arizona. NMH was supported by the  European Union 's  Horizon 2020 research and innovation program  under the  Marie Sklodowska-Curie  grant agreement no. 746334. MDM acknowledges support from the  MIUR   Rita Levi Montalcini  programme. JCS considers this work a contribution to his VILLUM Investigator project " Biodiversity Dynamics in a Changing World " funded by  VILLUM FONDEN  (grant  16549 ) and his  Independent Research Fund Denmark | Natural Sciences  project  TREECHANGE  (grant  6108-00078B ).
Data availability All maps will be made available through https://unbiodiversitylab. org/ and on a data repository (https://dx. doi. org/10. 5281/zenodo. 5006332). The raw input data can be requested from the respective data providers, namely IUCN, GARD, Birdlife International, Royal Botanic Gardens, Kew and predicted plant range data will be made available as part of the BIEN initiative  46. The IUCN habitat type map used to construct the AOH is made available in the Supplementary Information. The carbon layers will be published openly in a separate data descriptor manuscript and are available upon request. Any additional raw data not listed can be made available from the authors upon reasonable request. Code availability Code to run comparable optimization analyses has been made available at https://github.
Data Collection During July and August 2018, we conducted in-person individual interviews with 921 adults (≥ 18 years) living in SVG. This sample constitutes 1. 3% of the country's adult population, according to the most recent census estimates (Ministry of Finance 2019). Our interviews were conducted on four of the country's eight inhabited islands: St. Vincent, Bequia, Mayreau, and Union Island. More important to determining the geographic patterns of consumption than the interview locations, however, were the hometowns that our respondents reported; we interviewed individuals claiming 69 hometowns throughout SVG, which we then grouped into geographical regions. The sparsely inhabited interior of the main island (Fielding & Ollivierre 2017) was not included in our survey. Participants were recruited through various methods including encounters in public spaces, personal introductions through our local contacts, and a snowball sampling method  (Coleman 1958-59).
Data analysis Data with identi able personal information removed were analyzed with six primary variables collected for each of the 921 individuals included in the nal study. Consumption of food products derived from small cetaceans and from humpback whales were each analyzed separately as primary outcomes. Respondents' age category, gender, home region, and occupational category were included as primary explanatory variables. Likelihood ratio tests were conducted to determine the overall signi cance of each factor and Akaike information criteria (AIC) values were used to compare model t and guide nal model selection. Given the binary outcome under consideration-whether a person does or does not consume food products derived from small cetaceans or humpback whales-logistic regression was used to generate estimated adjusted odds ratios and 95% con dence intervals for the increased or decreased odds of consumption based on the explanatory variables included in the best-tting model.
Acknowledgements The authors gratefully acknowledge the support of  Don Bergfelt  and  Fortune Sithole  from  Ross University School of Veterinary Medicine. We appreciate the assistance of student eld researchers from the  University  of the South who are not listed as coauthors:  Elissa Clark ,  Helena Kilburn ,  Isabel Kirby ,  Komal Kunwar ,  Lauren Newman , and  Connor Peach. This research would not have been possible without the partnership of the following local eld collaborators:  Alana Adams ,  Moriah Alves ,  Enrico Barker ,  Sheena Clouden ,  Samantha Ince ,  April Jacobs ,  Keaah Johnson ,  Otmar Marshall ,  Shakera Ollivierre ,  Aureil Phillips ,  Tiffany Phillips ,  Tishana Reid ,  Anthony Sargeant ,  Eugena Simmons , and  Simone Williams. Data collection was conducted with the approval of-but independently from -the appropriate Ministries of the  Government of St.
None of the centers involved in this study performed regular (by month or by trimester) systematic screening of pregnant women by RT-PCR during the study period; all testing was performed on the basis of clinical symptoms or before planned admissions (regular hospital admission or admission for labor and delivery). The study was approved by the appropriate ethical board for each recruiting center (approval nos. CE2020/206, CEROG 2020-OBST-1104, and IST DIPUSVSP-24-02-217), and informed consent was obtained when required by the relevant local regulations. Clinical data were routinely collected in real time in the patient's electronic medical records. Data were then extracted retrospectively for the study and merged into a dedicated, secured, and anonymized database based at the coordinating center.
A data control was performed before analysis and, if data were inaccurate or missing, the recruiting centers were contacted to correct the identified issues. All relevant local and Europe privacy regulations were respected. The collated data included maternal age, geographic origin, prepregnancy BMI, parity, smoking status, chronic arterial hypertension, diabetes mellitus type I or II, preexisting pulmonary diseases (such as asthma, tuberculosis, and previous pulmonary embolism [PE]), and preexisting renal or liver diseases (such as renal or hepatic insufficiency, polycystic kidney disease, single kidney, previous nephrectomy, viral hepatitis, and kidney or liver transplant). For SARS-CoV-2-positive patients, we also collected data on date of positive RT-PCR test, gestational age at the time of RT-PCR, reason for performing RT-PCR (symptoms or screening), hospital admission related to SARS-CoV-2 infection, and disease severity according to the National Institutes of Health (13).
After we performed 15 multiple imputations of missing data of the original datasets by using the mice package in R software (R Project for Statistical Computing, https://www. rproject. org/), we used the CBPS R package to perform the propensity score, estimating an average treatment effect using covariate balancing and requesting an exact match, which has been shown to be superior to traditional logistic regression approaches and boosted classification and regression trees  (17). We considered an absolute standardized difference (ASD) of <10%-15% to support the assumption of balance between the groups because it is not affected by sample size, unlike p values, and it can be used to compare the relative balance of variables measured in different units  (18). We calculated the mean and SD obtained after matching for continuous variables and the percentage for categorical variables.
Before matching of covariates, a p value of <0. 05 was considered statistically significant. Nevertheless, we had to correct for multiple testing with a Bonferroni correction. For the secondary outcomes, 10 comparisons were performed twice; therefore, we divided the p value by the number of comparisons to obtain the p value at which we considered a result significant (0. 05/10 = 0. 005). All secondary outcomes with a p value <0. 005 after matching were therefore considered significant. We used the coxme R package to estimate Cox proportional hazards models using the center as random effect on a subset of the data, for which the gestational age at the time of SARS-CoV-2 RT-PCR was collected (for maternal.
Data We use temperature and precipitation observations from the E-OBS database  (Haylock et al. , 2008). The data are available on a 0. 1×0. 1 • grid from 1950 to 2018. As an estimate of temperature and precipitation in northern France we average these two fields over a rectangle spanning 45. 5-51. 5 • N and 1. 5 • W-8. 0 • E (see Fig. 1 ). This region also includes parts of the UK, Germany, Belgium, and Switzerland and therefore does not exactly match the studied area of  (Ben-Ari et al. , 2018). The seasonal meteorological conditions we study here are related to large-scale events, and averaging over a larger rectangle therefore seems appropriate. We use the reanalysis data of the National Centers for Environmental Prediction (NCEP)  (Kistler et al. , 2001)  for the analysis of atmospheric circulation.
We consider the geopotential height at 500 hPa (Z500) and mean sea level pressure (SLP) over the North Atlantic region for computation of circulation analogues and a posteriori diagnostics. We used the daily averages between 1 January 1950 and 31 December 2018. The horizontal resolution is 2. 5 • in longitude and latitude. The rationale of using this reanalysis is that it covers 70 years and is regularly updated. One of the caveats of this reanalysis dataset is the lack of homogeneity of assimilated data, especially before the satellite era. This can lead to breaks in pressure-related variables, although such breaks are mostly detected in the Southern Hemisphere and the Arctic region  (Sturaro, 2003)  and marginally impact the eastern North Atlantic region. Z500 patterns are well correlated with western European temperature and precipitation because those quantities and their extremes are related to the atmospheric circulation  (Yiou and Nogaj, 2004; Cassou et al.
Acknowledgements. We acknowledge the E-OBS dataset from the  EU-FP6  project  UERRA  (http://www. uerra. eu,last access: 20 September 2019) and the  Copernicus Climate Change Service  and the data providers of the  ECAD  project (https://www. ecad. eu,last access: 20 September 2019). We also acknowledge the  NCEP Reanalysis data  provided by the  NOAA/OAR/ESRL. Financial support. This research has been supported by the  DAMOCLES COST  (grant no. CA17109 ). Peter Pfleiderer  was supported by the  German Federal Ministry of Education and Research  (grant no. 01LN1711A ). Edoardo Vignotto  was supported by the  Swiss National Science Foundation  (Doc. CE11 Mobility Grant no. 188229 ). The publication of this article was funded by the  Open Access Fund of the Leibniz Association. Review statement.
This study aims at investigating the development of premature infants' autonomic nervous system (ANS) based on a quantitative analysis of the heart-rate variability (HRV) with a variety of novel features. Additionally, the role of heart-rate drops, known as bradycardias, has been studied in relationship to both clinical and novel sympathovagal indices. ECG data were measured for at least 3 hours in 25 preterm infants (gestational age ≤ 32 weeks) for a total number of 74 recordings. The postmenstrual age (PMA) of each patient was estimatedfrom the RR interval time-series by means of multivariate linear-mixed effects regression. The tachograms were segmented based on bradycardias in periods after, between and during bradycardias. For each of those epochs, a set of temporal, spectral and fractal indices were included in the regression model.
Preprocessing The HRV represents the instantaneous fluctuations of heart rate and is usually expressed by the tachogram which visualizes the variations of the time interval between two consecutive R-peaks (RR intervals, RR i ). In order to compute a RR i time series, the R peaks of the ECG have been detected via the Matlab toolbox by  Moeyersons et al. (Moeyersons et al. , 2019) , which is based on enveloping procedure. This graphical user-interface also allows for correction and deletion in case of erroneous R-peaks. In case of a single missing R-peak, the value was replaced by using the formula: Rt = R t-1 + R t+1 2 , ( 1 ) where Rt is the estimated position of the missed R-peak, while R t-1 and R t+1 are the location of the previous and following R-peak. In case of two or more missing R-peaks due to ECG flat lines or muscle artifacts which made the QRS detection impossible, the contaminated parts of the signal were discarded.
The average duration and amplitude of a bradycardia event is reported in Table  1 , which also shows the average number of bradycardias in the entire dataset. A full overview recording by recording is reported in the supplementary Table  S1  and S2, which display the number of bradycardias as well as the average intensity and the average duration of heart-rate drops patient by patient. The supplementary materials show that some of the recordings did not have any heart-rate drop according to the reported definition. Therefore, the windowing scheme based on bradycardias was not applicable. In this specific case, the design choice was a segmentation in non-overlapping 10 minutes windows and assign the results of feature extraction to post-bradycardia windowing scheme (see Figure  2 ).