dataesr/scientific-paragraphs-categorization huggingface dataset:
Methods We did a systematic review and meta-analyses on invasive GAS outcomes, including incidence, case fatality risks, and neurodevelopmental impairment risk, among pregnant women, neonates (younger than 28 days), infants (younger than 1 year), and children (younger than 5 years) worldwide and by income region. We searched several databases for articles published from Jan 1, 2000, to June 3, 2020, for publications that reported invasive GAS outcomes, and we sought unpublished data from an investigator group of collaborators. We included studies with data on invasive GAS cases, defined as laboratory isolation of Streptococcus pyogenes from any normally sterile site, or isolation of S pyogenes from a non-sterile site in a patient with necrotising fasciitis or streptococcal toxic shock syndrome.
For inclusion in pooled incidence estimates, studies had to report a population denominator, and for inclusion in pooled estimates of case fatality risk, studies had to report aggregate data on the outcome of interest and the total number of cases included as a denominator. We excluded studies focusing on groups at very high risk (eg, only preterm infants). We assessed heterogeneity with I². Findings Of the 950 published articles and 29 unpublished datasets identified, 20 studies (seven unpublished; 3829 cases of invasive GAS) from 12 countries provided sufficient data to be included in pooled estimates of outcomes. We did not identify studies reporting invasive GAS incidence among pregnant women in low-income and middleincome countries (LMICs) nor any reporting neurodevelopmental impairment after invasive GAS in LMICs.
A review in 2005 included data from only one LMIC (Kenya)  9  and, since then, additional studies from Fiji and Kenya have identified high incidences of invasive GAS among children, particularly neonates and infants. 17, 18  Understanding the incidence of invasive GAS and associated death and disability during pregnancy and childhood is essential to direct resources, such as vaccines that are in preclinical development or phase 1 clinical trials (eg, NCT02564237), appropriately. To the best of our knowledge, there has been no previous metaanalysis of invasive GAS incidence among pregnant women and children worldwide, including outcomes of disability and death. Therefore, we aimed to calculate.
Search strategy and selection criteria We searched for publications on invasive GAS incidence published from Jan 1, 2000, to June 11, 2019, with the search updated on June 30, 2020. Searches for invasive GAS outcomes in pregnancy were done separately to outcomes in children. We searched MEDLINE, Embase, Global Health, Scopus, Web of Science, WHO Library Information System, Africa Wide Information, and WHO regional databases (Index Medicus for South-East Asia Region, Index Medicus for the Eastern Mediterranean Region, Medcarib [Latin American and Caribbean database], and Western Pacific Region Index Medicus), and three grey literature databases (Open Grey, Greyline, and OpenTrials). We searched trial registries using the OpenTrials database and we searched the reference lists of relevant reviews.
Search terms included "incidence", terms related to early childhood or pregnancy, "group A streptococcus" or "Streptococcus pyogenes", and terms related to invasive infections (appendix 1 pp 3-6). We included studies with data on invasive GAS cases, which were defined as laboratory isolation of S pyogenes or GAS from any normally sterile site or isolation of GAS from a non-sterile site in a patient with necrotising fasciitis or streptococcal toxic shock syndrome. For inclusion in pooled incidence estimates, studies had to report a population denominator; for inclusion in pooled estimates of case fatality risk, studies had to report aggregate data on the outcome of interest and the total number of cases included as a denominator (obtained from the publication or from investigators on request).
For the qualitative synthesis (appendix 1 p 7), we did not exclude studies that stated incidence of invasive GAS without providing a population denominator. We excluded studies focusing on groups at very high risk (eg, only preterm infants), not representative of the local, regional, and national demography in the community. When there were duplicate data (ie, data from the same population reported in multiple studies), we included only the most recent study. We did not apply any language exclusions and translated papers using Google Translate if necessary. For unpublished data, we approached 198 researchers (in 48 countries) working in the fields of maternal, neonatal, and paediatric infectious diseases to form an investigator group to contribute unpublished aggregate data eligible for inclusion or to do secondary analyses of published data to enable inclusion.
Data selection and abstraction Three researchers conducted searches of the literature (ES, SV, and IK). We developed a Microsoft Excel template (appendix 3) to systematically abstract data on study design, location, publication date, period of data collection, case ascertainment methods, and definition of invasive GAS. We used these data to assess each study on its methods, ensuring it met inclusion criteria and that there were no reasons for exclusion. We also assessed studies for inclusion against a standard template for prevalence and incidence studies. 19 e recorded the incidence of GAS (for qualitative analyses) and number of cases of invasive GAS and outcomes (ie, death, disability, stillbirth, and miscarriage) if available for meta-analyses. We extracted the number of livebirths and person-years for the denominator for incidence of invasive GAS.
Results We identified 950 published articles and 29 unpublished datasets for consideration. Following the removal of 285 duplicates and 492 at title and abstract review stages, 173 full-text articles or unpublished datasets were assessed for eligibility (figure  1 , with search outcomes for pregnancy, children, and neurodevelopmental impairment in appendix 1 pp 23-25). 35 studies (comprising 28 published articles and seven unpublished datasets) from established regional or national disease surveillance programmes were included in the qualitative synthesis (characteristics summarised in the table, with a quality assessment in appendix 1 pp 17-21). Regional or national surveillance programmes contributing data included the USA's multiregional Active Bacterial Core surveillance system, Public Health England (now the UK Health Security Agency), Santé publique France, Finland's National Institute for Health and Welfare, Canada's British Columbia Centre for Disease Control, the Norwegian Institute of Public Health, and the USA's regional Arctic Investigations Program.
Four  18, 24, 31, 38  of 28 published articles identified did not contain all data needed for inclusion in our study (eg, different age groups). Therefore, we approached these authors to request provision of additional data that they, or we, could conduct secondary analyses on to include in our meta-analyses. Authors from all four studies provided data. Of the 35 studies and datasets included in qualitative analyses (appendix 1 p 7), 15 were not included in the meta-analyses because they provided incidence but no numerator or denominator to enable calculation of pooled estimates in our meta-analyses. Therefore, 20 studies and datasets (including seven unpublished datasets) from 12 countries (Canada, Finland, Fiji, France, Ireland, Israel, India, Kenya, Norway, South  cases through analysis of existing database or surveillance systems, seven through analysis of laboratory results, and one through a combination of methods (table and appendix 1 p 10).
We included nine studies with data on invasive GAS in pregnancy and the post-partum period (all in HICs) in the meta-analysis, one of which was unpublished. There were 650 cases of invasive GAS in pregnant or post-partum women in 7 645 063 livebirths. Three published studies  22, 38, 44  included only post-partum cases. One published study  39  reported septic abortions in three (10%) of 28 cases of maternal invasive GAS infections occurring during pregnancy and the post-partum period; however, no other published studies reported incidence (or non-occurrence) of septic abortions, miscarriages, or stillbirths. The one unpublished dataset reported induced abortions or stillbirths in 35 (10%) of 334 maternal invasive GAS cases and neonatal deaths in seven (2%) cases. Four studies reported incidence of neonatal invasive GAS coinfection in mothers with invasive GAS; of these studies, three reported no episodes of neonatal infection  22, 39, 41  and one reported invasive GAS coinfection in two (1%) of 134 neonates.
33 n studies included in the meta-analysis, the reported incidence of invasive GAS was lowest in Ireland, at 0•05 (95% CI 0•05-0•05) per 1000 livebirths,  25  and highest in west Norway,  38  at 0•30 (0•29-0•30) per 1000 livebirths (figure  2A ; appendix 1 p 22). Of the three studies that reported data on deaths, two  38, 39  published studies reported no deaths and one unpublished dataset (Active Bacterial Core Surveillance System, unpublished) reported deaths in nine (3%) of 334 cases among pregnant or post-partum women (appendix 1 p 22). We did not find any data on    44  Daneman et al (2005)  22  Drew et al (2015)  25  Leonard et al (2019)  33  Oppegaard et al (2015)  38  ABCs (2016) † Rottenstreich et al (2019)  39  Shinar et al (2016)  41  Knowles et al (2015 ).
Canada We identified 11 studies reporting invasive GAS incidence among neonates, including three published studies in LMICs (India [only included in-hospital births], Kenya, and South Africa; table, figure  2B ; appendix 1 p 22). We identified one published and seven unpublished datasets from HICs. Across all studies, incidence of invasive GAS was lowest in South Africa (with no cases) and highest in Kenya (0•30 [95% CI 0•29-0•30] cases per 1000 livebirths; figure  2B ; appendix 1 p 17). We did not find any data on neurodevelopmental impairment in neonates that met our inclusion criteria. We found three published and five unpublished datasets with neonatal case fatality risk data (figure  3A ). The lowest case fatality risks among neonates were in the USA (Alaska), Norway, and Finland (all 0%), and the highest case fatality risk was in India (80%; figure  3A ; appendix 1 p 17).
We calculated the pooled incidence of neonatal invasive GAS disease worldwide as 0•04 (95% CI 0•03-0•05) per 1000 livebirths (figure  2B ; appendix 1 p 22). Incidence was 0•12 (0•00-0•24) per 1000 livebirths in LMICs and 0•02 (0•00-0•03) per 1000 livebirths in HICs (figure  2B ; appendix 1 p 17). Heterogeneity was high (I²=100%). Overall, the case fatality risk was 21% (95% CI 3 to 38; figure  3A ). The case fatality risk was estimated to be 61% (33 to 89) in neonates in LMICs and 3% (-3 to 10) in neonates in HICs (figure  3A ; appendix 1 p 17), although there was consi derable uncertainty around the central estimates. We included four published studies and six unpublished datasets reporting incidence of invasive GAS in infants in the meta-analyses (figure  2C ). Only two studies were in LMICs (in Kenya and South Africa).
Overall, 1193 infants in 138 44 860 livebirths developed invasive GAS disease. Incidence was lowest in Norway (0•03 [95% CI 0•03-0•03] per 1000 livebirths) and highest in Kenya (0•61 [0•61-0•61] per 1000 livebirths; figure  2C ; appendix 1 p 17). Two Australasian studies reported very high incidence of invasive GAS in infants (0•33 per 1000 population in New Zealand, and 1•23 per 1000 in Indigenous populations and 0•12 per 1000 in non-Indigenous populations in Australia) but did not meet the inclusion criteria for the meta-analysis (appendix 1 p 10). We did not find any data on neurodevelopmental impairment that met our inclusion criteria. We found three published and four unpublished datasets reporting case fatality risks in infants. The case fatality risk was lowest in South Africa (0% [0-84]) and highest in Norway (33% [1-91]) and Kenya (33% [22-46]; figure  3B ; appendix 1 p 17).
We calculated the pooled incidence of invasive GAS disease in infants worldwide as 0•13 (95% CI 0•10 to 0•16) per 1000 livebirths (figure  2C ). Incidence was higher in LMICs (0•33 [-0•22 to 0•88] per 1000 livebirths) than in HICs (0•08 [0•05-0•11] per 1000 livebirths; figure  2C ; appendix 1 p 22); however, the 95% CIs overlap. Heterogeneity was high (I²=100%). Overall, the case fatality risk was 14% (95% CI 4-25, figure  3B ) overall, 23% (-7 to 53) in LMICs, and 7% (2-11)  in HICs (figure  3B ; appendix 1 p 22), although only two studies were included from LMICs and 95% CIs are overlapping. We included eight studies reporting invasive GAS incidence among children aged 1-5 years in the meta-analysis, of which six were unpublished datasets and two were published. A total of 2019 cases were reported in 51 501 598 person-years.
Incidence among children aged 0-5 years was lowest in France (0•02 [0•02-0•02] per 1000 person-years) and highest in Fiji (0•27 [0•26-0•27] per 1000 person-years; figure  4B ; appendix 1 p 22). We did not find any data on neurodevelopmental impairment for children aged 0-5 years that met our inclusion criteria. Case fatality rates among children aged 1-5 years were reported in two published studies and four unpublished datasets. The lowest case fatality rate was in Norway (0% [0-18]) and the highest was in Kenya (22% [9-40]; figure  3C ; appendix 1 p 22). Case fatality rates in children aged 0-5 years were from the same six papers, with the lowest case fatality rate in the USA (3% [2-5]) and the highest in Kenya (30% [21-40]; figure 3D appendix 1 p 22). The pooled incidence of invasive GAS in children aged 1-5 years worldwide, based on available data, was 0•04 (95% CI 0•03-0•05) per 1000 person-years (figure  4A ).
Significant gaps remain in understanding the response of plant reproduction to environmental change. This is partly because measuring reproduction in long-lived plants requires direct observation over many years and such datasets have rarely been made publicly available. Here we introduce MASTREE+, a data set that collates reproductive time-series data from across the globe and makes these data freely available to the community. MASTREE+ includes 73,828 georeferenced observations of annual reproduction (e. g. seed and fruit counts) in perennial plant populations worldwide. These observations consist of 5971 population-level time-series from 974 species in 66 countries. The mean and median time-series length is 12. 4 and 10 years respectively, and the data set includes 1122 series that extend over at least two decades (≥20 years of observations).
For a subset of well-studied species, MASTREE+ includes extensive replication of time-series across geographical and climatic gradients. Here we describe the open-access data set, available as a. csv file, and we introduce an associated web-based app for data exploration. MASTREE+ will provide the basis for improved understanding of the response of long-lived plant reproduction to environmental change. Additionally, MASTREE+ will enable investigation of the ecology and evolution of reproductive strategies in perennial plants, and the role of plant reproduction as a driver of ecosystem dynamics.
g. failure/partial/full crop) which can be successfully used to investigate the variability and synchrony of plant reproduction  (Bogdziewicz et al. , 2021). In total, 974 species are represented, drawn from 136 families across the plant Tree of Life. This increases species representation by 168% compared with the largest previously available compilation  (Pearse et al. , 2020) , which is incorporated into MASTREE+. This expands the potential to quantify reproductive traits that describe the spatiotemporal variability of reproduction (i. e. masting) with other life-history traits to better understand the evolution of plant reproductive strategies  (Dale et al. , 2021; Fernandez-Martinez et al. , 2019; Pesendorfer et al. , 2021). For example, we have 67 species overlap with the plant demographic database COMPADRE  (Salguero-Gomez et al.
, 2015) , 442 species overlap with seed mass data from the Kew Seed Information Database  (Royal Botanic Gardens Kew, 2021)   (11). We include data from 66 countries, six continents (Figure  2 ), and from all the major vegetated biomes (Figure  3 ). Importantly, we increase data representation from regions that have been unrepresented in previous data sets  (Ascoli, Maringer, et al. , 2017; Pearse et al. , 2020) , including south and central America, Africa, and Asia, although these regions remain strongly under-represented. Sampling intensity varies between species. 3 ). These records will enable investigation of intraspecific variation in plant reproduction across climate, space, and time, including trends in the spatiotemporal variability of reproduction. It will also enable comprehensive assessments of intraspecific variability of masting characteristics (i.
Resolving this uncertainty is a priority because changes in seed masting will impact plant reproductive success, and more widely affect ecosystem services and habitat management  (Ida, 2021; Pearse et al. , 2021; Touzot et al. , 2020). In systems where seed production limits recruitment, MASTREE+ can be utilised to understand the drivers of plant reproduction and regeneration  (Abraham et al. , 2018; Manríquez et al. , 2016; Oliva et al. , 2013). Intraspecific differences in fecundity and masting influence regeneration success, determining species composition and vegetation structure, including during the colonisation of new habitats  (Joubert et al. , 2013) , and after natural and anthropogenic disturbance  (Martin-DeMoor et al. , 2010; Mokake et al. , 2018; Peters et al. , 2005). Masting characteristics of hundreds of species can be investigated using MASTREE+, and integration with plant trait and demographic databases will enable deeper integration of masting and reproductive strategies within life history theory (Salguero-Gomez F I G U R E 2 The geographical distribution of time-series within MASTREE+.
The scale of spatial synchrony of masting appears to be highly variable between species  (Bogdziewicz et al. , 2019) , but this has only been quantified of a handful of species so far  (Koenig & Knops, 2013; LaMontagne et al. , 2020). In masting species, highly variable allocation to reproduction has wider effects on plant resource allocation, and carbon and nutrient cycling through ecosystems, but this remains poorly explored  (Brumme et al. , 2021; Khanna et al. , 2009; Muller-Haubold et al. , 2015). Data in MASTREE+ can be combined with existing field and remote-sensing data sets of plant growth or productivity, and with data sets of whole-ecosystem or soil carbon and nutrient fluxes to existing or retrospective sampling (e. g. tree-rings) to address outstanding question regarding resource allocation between growth, reproduction, and defence, particularly how this varies interspecifically and with environmental stress, and how this may shape species and community responses to environmental change  (Lauder et al.
| DATA SOURCE S , ACQU IS ITI ON AND COMPIL ATION We collected species-specific time-series of annual reproductive effort for terrestrial perennial plant populations, including trees, shrubs, herbs and grasses. We included data from unmanaged and managed populations, but excluded agricultural crop species subject to selective breeding. Where reproduction was monitored under experimentally manipulated conditions (e. g. fertilisation, warming, rainfall exclusion), we only included data from control plots. Data were collected for reproductive effort at different stages of the reproductive cycle (e. g. flowers or inflorescences, pollen abundance, number of fruits, cones or seeds), but 90% of data were mature seed, fruit, or cone production. We did not set a minimum time-series length but prioritised compiling effort on time-series ≥4 years.
All time-series represent reproductive effort at the population level, ranging from local populations with <10 individuals to regional estimates of reproduction, and we recorded information on the number of monitored individuals in each population and the spatial scale represented by the time-series (Table  1 ). We also included information on the original data collection methods, which included litter traps (19. 3% of all records), seed, cone and fruit counts (18. 3%), other methods including estimates of cone production using cone or fruit scars and categorical classification of seed and fruit crops by wildlife managers or foresters. Data were collected from several sources. We harmonised data from previously published compilations of plant reproductive effort displaying differences in data architecture  (Ascoli et al.
, 2020; Ascoli, Maringer, et al. , 2017; Pearse et al. , 2020). To identify other time-series, we searched Google Scholar and Scopus with multiple combinations of search terms (see  Appendix 2). Spanish-and French-language searches was used to increase data representation from South America and Africa. An initial screen was based on where data were contained in figures, we used the WebPlotDigitizer tool  (Rohatgi, 2020). Metadata associated with each time-series was also extracted from the sources, or directly from data set contributors, and copies of original sources were archived.
| Technical validation and quality control A two-stage approach was adopted to validate time-series data. Initially, we standardised attribute data and checked for errors and inconsistencies within time-series. Species names were checked and standardised to The Plant List nomenclature, using the 'Taxonstand' package for R (v. 2. 3)  (Cayuela et al. , 2021). Country names were converted to the English short name (ISO3166-1) using the 'countrycode' package for R (v. 1. 2. 0)  (Arel-Bundock et al. , 2018). Automatic checks were performed to ensure that each time-series was uniquely identified by the identification variables and that time-series' observations were uniquely identified by Year. Species_code was assigned by automatically combining the first three characters from the TPLstandardised genus and species names.
Where separate species shared a Species_code, a unique combination was manually created. The final character of Species_code for populations of a hybrid origin was changed to 'X'. We ran various automatic checks to ensure all observations in a time-series had uniform attribute data where such uniformity was expected (i. e. within a time-series, there was only a single value for variables such as Unit). Interrelated variables were checked to ensure consistency, for example that time-series spatial data (Latitude, Longitude) fell within the boundaries of the indicated Country. Time-series duration variables (i. e. Segment, Start, End, Length) were directly calculated from time-series. The second stage involved the detection and removal of duplication problems between time-series, that is, series added multiple times, including with partial overlap, usually when data were published in more than one source.
The contribution of TX was supported by the National Natural Science Foundation (Grant No. 32001310) and Chuzhou University Start-up Foundation for Research (2021qd05). Data from Italian Central Apennines were available, thanks to the support of the research project 'Monitoraggio della produzione di seme di specie forestali, rinnovazione naturale e relazioni con la fauna selvatica (Pasciona)' funded by the Foreste Casentinesi, Monte Falterona e Campigna National Park. The contribution from RJD would not have been possible without the effort of Dr. Theodore W. ('Doc') Daniel. Doc Daniel had the long-term vision to commence and oversee the annual cone counts for hundreds of trees from 1947-1981 on the Utah State Agricultural College (now Utah State University) School Forest, later named in his honour as the T.