dataesr/scientific-paragraphs-categorization huggingface dataset:
Evaluation of palaeoclimate reconstructions With the development of recent statistical tools and computing power, it has become easier to produce quantitative reconstructions, thus shifting the complexity of the work from the extraction of signal from multivariate pollen data to the development of sensible modern datasets and the evaluation of the reconstructions. Evaluating a reconstruction is a complex process that relies on both statistical indicators and expert knowledge. Commonly used indicators include: 1) the amplitude of change (e. g. Is the reconstructed amplitude of change coherent with expectations considering the location and studied time period?), 2) the consistency of the reconstruction across time (e. g. Is the sample-to-sample variability more important than the reconstructed signal itself?) or 3) the percentage of explained variance (e.
High diversity samples tend to have a higher SCD with modern samples than low diversity samples. Using the 5th percentile of the measured distance between modern dataset samples has become the standard threshold identifying fossil samples with good modern analogues (e. g. Jackson and Williams, 2004). While imperfect, this empirical approach can be employed consistently and is justifiable. Mapping the selected analogues in geographical space can help to better understand the drivers of the reconstruction, as it allows to reconnect with the ecology supporting the reconstructions. Looking at the spread of the uncertainties around the reconstructed value can also be a good indicator of the quality of the reconstruction, especially with methods that estimate the distribution of climate histories (e.
g. Kühl et al. , 2007). Another evaluation tool is the palaeoSig test developed by  Telford and Birks (2011c) , which assesses whether a reconstruction shows significant changes in the inferred variable compared to a null distribution of reconstructions derived from transfer functions trained on simulated climate variables. The palaeoSig test compares the percentage of variance explained by a reconstruction to an ensemble of reconstructions obtained from a randomised calibration dataset. If the real reconstruction explains more variance than most of the randomised reconstructions (e. g. 95% of them), the reconstruction is deemed statistically significant  (Telford and Birks, 2011c). However, palaeoSig does not test if the reconstructed values make sense climatically; it only tests if there is a statistically significant trend in the data relative to an ensemble of random reconstructions.
Climate reconstructions with low climate variability will unavoidably 'fail' the palaeoSig test. This does not necessarily mean the reconstruction is incorrect, it simply means that there is no statistically significant trend in the data (i. e. the reconstruction could be reliable, but climate may have been stable over the studied period). Alternatively, in the presence of a confounding factor, reconstructions can 'pass' the palaeoSig, while being erroneous  (Juggins, 2013). As with any evaluation tool, palaeoSig should only be one element used to assess the reliability of a climate reconstruction. Multi-site, multi-technique or multi-proxy reconstructions can also be used as an additional way of evaluating reconstructions. For multisite comparisons (comparing a reconstruction with independent regional records of the same variable), expecting good correlation between records is reasonable, as climate is spatially autocorrelated.
Multi-technique approaches (different reconstruction techniques applied to the same pollen dataset) usually produce similar trends but can be characterised by different amplitudes of change or intrinsic noise  (Brewer et al. , 2008; Lebamba et al. , 2012; Peyron et al. , 2013). While differences in amplitude can be hard to reconcile and can be caused by many factors, they have been interpreted as an estimate of the climate uncertainty associated with the reconstruction. Composite reconstructions can be obtained by combining the reconstructions obtained with the different techniques  (Birks, 2014). Finally, highlighting similarities in trends and amplitude between multi-proxy reconstructions (reconstructing the same variable from one core or nearby locations using different proxies) can provide additional support  (Birks and Ammann, 2000; Finsinger et al.
g. local mesoclimate, low number of analogues, uncertain dating), but overall, climate is spatially correlated, and the reconstructions should reflect that. Any strong departure from this may reflect that either the modern dataset was inappropriate or that the reconstructed variable is not the main driver of the pollen changes in the record. It can also mean that the two records being compared do not reflect the same climatic aspects, which may require a re-assessment of the interpretation of the other record. In conclusion, there is no single solution that exists to either validate or invalidate a pollen-based climate reconstruction. It is only through critical consideration of multiple, independent lines of evidence (climatic and ecological, multi-site, multi-proxy and multi-techniques intercomparisons, palaeoSig, analogue distance, etc.
Modern and fossil pollen databases Limited or difficult access to modern and fossil pollen data, metadata and modern plant distributions have often hindered the application of climate reconstruction techniques. Until recently, open-access pollen data have mainly been gathered into independent, individually structured regional databases (Table  2 ) that were not directly compatible with each other. Over the past decade, the Neotoma Paleoecology Database, a global database that includes pollen as well as other palaeoecological proxies, has been gradually incorporating pollen data from many of these regional databases to facilitate access and usability of the data  (Williams et al. , 2018a). Importantly, the taxon names of the various datasets have been standardised in Neotoma to follow new international standards and facilitate the use and combination of these datasets, even if the constituent databases retain their own identities and governance to ensure that the regional specificities are not overly simplified.
All the datasets in Neotoma are also curated to become more compliant with the FAIR principles (FAIR: Findable, Accessible, Interoperable and Reusable;  Wilkinson et al. , 2019 , Wilkinson et al. , 2016). Neotoma is continually growing and presently contains over 4700 fossil and ~3600 modern pollen records all over the world (as of August 2020).
Name Reference(s) Source(s) Modern and Fossil pollen databases /datasets Despite this effort, access to some regional databases continues to be problematic, as they are not available from well-identified public repositorries. Many small-scale regional modern sample datasets also continue to be developed and are typically available outside of Neotoma (e. g. Julier et al. , 2018; Montade et al. , 2019b; Montade et al. , 2019a; Urrego et al. , 2015). Supporting further field campaigns, especially in quantification deserts, and making these data publicly accessible (e. g. by adding them to relevant regional Constituent Databases in Neotoma, or any other repository, such as Pangaea www. pangaea. de or NOAA www. ncdc. noaa. gov) is critical to improve the global coverage of modern pollen assemblages and, by extension, the applicability of climate reconstruction methods.
To our knowledge, only the Global Biodiversity Information Facility (GBIF) provides a set of globally dense distributions of plants that can be directly used with the pdf techniques  (Chevalier, 2019; Chevalier, 2018). Other high-resolution datasets of the distribution of most European trees  (Mauri et al. , 2017)  and North American (Biodiversity Information Serving Our Nation (BISON) and US Forest Inventory and Analysis (FIA)) vegetation are also available (see Table  2 ). Additional collections can often be obtained from herbaria and national museums by direct request.
Modern climate data Modern climate datasets are used by all the techniques to define the relationships between plant/pollen taxa with climate at present time, following the standard space-for-time assumption that spatial relationships among plant taxa and environmental variables can be used to draw inferences from fossil pollen time series about past environmental variations (Section 3). The most commonly used datasets are listed in Table  2. These datasets average climatic conditions over different time periods. For instance, WorldClim 2. 0 reports average values of the period 1970-2000, while CHELSA represents the period 1979-2013. Generally, all modern samples are assigned climate values based on the same climate data. To provide a common background for climate reconstructions, many pollen calibration datasets are already associated with a specific climate dataset.
For instance, in the latest version of the Eurasian Modern Pollen Database, the samples have been paired with 30 arcsecond WorldClim 2. 0 data  (Davis et al. , 2020). However, the climate baseline associated with the earliest modern samples collected in 1970s and 1980s and the most recent ones has shifted, especially considering contemporary climate warming. This potential disequilibrium between vegetation and climate may challenge the first fundamental assumption in regions where climate has changed the most over the past decades (Section 3. 1). In addition, climate data are often available at different spatial resolutions ranging from 1° or 2° to much finer 30 arcsecond grids and there is no consensus regarding the choice of the optimal spatial resolution, which can impact the reconstruction  (Fortin and Gajewski, 2012; Ladd et al.
, 2015). For instance,  Ladd et al. (2015)  do not recommend using high-resolution modern datasets, such as the World-Clim 30 arcsecond (~1 km) grid, for broad-scale climate reconstructions. They argue that the resolution should generally not be finer than the area represented by the data in the training set (i. e. source area for modern pollen samples as described in Section 3, or resolution of modern plant distribution data). Similarly, pdf studies have upscaled the climate data to match the lowest resolution of the botanical data employed (e. g. Truc et al. , 2013). The selected resolution is particularly relevant for mountainous regions where climate varies strongly over short elevational distances  (Furlanetto et al. , 2019)  but is less important in instances when climate fields are spatially smooth (e.
Software Several software packages for carrying out modern calibrations and transfer function techniques have been developed (Table  2 ). Most of them have been written in the open source R statistical computing language (R Core Team, 2018) and are available online via GitHub / CRAN. These currently include rioja, analogue, vegan, MATTOOLS, BIOINDIC, Bclim and the palaeoSig package for testing the statistical significance of climate reconstructions. MAT-capable software with a graphical user interface include Polygon and Past. For Windows users, most techniques can be implemented in C2 without the need for programming. The pdf-techniques can be applied using the CREST python software (a R package is currently under development) and the CRACLE model can be applied using the cRacle R package.
Many of the Bayesian techniques described in Section 4. 4 can be applied from the Bayes-Composition R package on gitHub, although at this time, it is still under development and the documentation may be incomplete (J. Tipton, pers. comm. ). The Bayesian model Bclim can be used using the eponym R package  (Parnell et al. , 2016b). All data stored in Neotoma can be accessed through a dedicated R package  (Goring et al. , 2015) , thus facilitating the connection with the reconstruction methods.
Looking ahead: Opportunities and next steps Reliable and accurate palaeoclimate reconstructions are necessary to address the issues posed by modern climate change. As a result of the significant increase of curated palaeodata through online, open-access databases and repositories during the last decade, three important avenues of development for pollen-based climate quantifications have emerged: 1) Developing new techniques using the wealth of available data to extract more reliable information from fossil pollen records, 2) Providing tools to facilitate access to and use of the reconstruction techniques and 3) Developing and using new datasets and techniques to produce more reconstructions in quantification deserts. Building on the global abundance of pollen records, the development of such new frameworks and their increased use will contribute to better understand past climate variability across the planet, from local to continental scales.
Harnessing open palaeodata to move from site-based to network-based reconstructions The methodologies presented here all reconstruct climate from each sample/record independently from the others, that is without considering what may have happened before or after the time of deposition, or at nearby locations. The unprecedented density of easily accessible pollen records available from open-access, online databases (Table  2 ) can now support the transition from such sample-to-sample independent reconstructions to more holistic approaches in which samples would no longer be reconstructed in isolation. Some efforts have already been made in that direction and many studies have shown that combining multiple site-based reconstructions enabled extracting robust shared temporal trends (e. g. Chevalier and Chase, 2015; Davis et al.
Improving reporting standards to increase global use and methodological transparency The many decisions that must be taken before and after applying any of reconstruction technique (e. g. identifying taxa to be used, selecting the number of analogues, selecting the number of components of the model, validating a reconstruction and accessing and formatting appropriate modern datasets; Section 4), require expertise to produce reliable reconstructions  (Juggins, 2013). Accessing and formatting appropriate modern datasets (Section 5) is another hurdle that complicates access to and likely limits the use of these tools. Increasing the level of details about the different steps of the reconstruction process reported in the publications, from the design of the modern dataset to the assessment of the reliability of a reconstruction, could help to overcome these constraints.
The required information is usually distilled in the text but grouping it into one single and standardised table and/or graphical output would help to quickly assess how the model was run and help potential users to get a better understanding of the process. Table  3  summarises some parameters and data treatment decisions that are essential to report. As most of the techniques are already available as R packages and/ or software solutions (Section 6), implementing standardised log outputs to summarise the procedure, such as a publication-ready table or figure, directly in these numerical tools represent a key step to standardise reporting. Similar approaches have been adopted by other statistical models commonly used, such as the age-depth modelling technique Bacon  (Blaauw et al. , 2020; Blaauw and Christen, 2011 ) that automatically generates graphical plots summarizing the selected parameters.
Technique Modern dataset The model Ultimately, the ideal approach would be to comply with the FAIR principles by releasing any new data used, listing the provenance of existing publicly available data, and sharing the code used to format the modern and fossil pollen data and reconstruct the climate variable(s). Sharing scripts is the most transparent solution and the simplest way to describe all the data manipulations that were necessary to obtain climate reconstructions. As such, it is recommenced to prefer programmatic interfaces over point-and-click programs to support reproducibility of the user's workflow. Many online platforms now exist for basic script sharing such as GitHub (https://github. com) or Bitbucket (https://bitbucket. org), but also for more user-friendly interface, such as RMarkdown annotated scripts (https://rmarkdown.
Resolving quantification deserts Fossil pollen records have been retrieved from all continents. In contrast, pollen-based climate reconstructions are highly concentrated in the Northern Hemisphere extratropics (Fig. 1 ). While this discrepancy can be partly explained by the absence of modern pollen datasets in many regions (Fig. 10 ) and the belief for a long-time that vegetation in the tropics was stable  (Flenley, 1979) , the limited access to the necessary resources and technical knowledge required to use climate reconstruction techniques has further contributed to the persistence of large quantification deserts. However, the recent trends towards open-access and online databases coupled with the continuous development of new reconstruction techniques based on alternative principles (e.
Conclusion This paper reviews over 75 years of the development and application of quantitative pollen-climate reconstruction methods since the pioneer reconstruction of  Iversen (1944). The advance of increasingly sophisticated statistical and computational modelling approaches to pollen-climate analysis paralleled by rapid improvements in the size, quality and availability of modern datasets has provided a wealth of information about the past climates of many regions (e. g. Bartlein et al. , 2011; COHMAP Members, 1988; Marsicek et al. , 2018). These developments in methods and datasets have also been driven by demand for more spatially extensive climate reconstructions from the climate modelling community that requires palaeodata in climate model evaluation (e. g. Harrison et al. , 2015).
Despite the successful development of alternative climate proxies in recent years -including those based on isotopes and biomarkers -fossil pollen remains the most important source of quantitative climate data for terrestrial un-glaciated regions (e. g. Kaufman et al. , 2020). Very few climate proxies can rival pollen for its spatial and temporal coverage for the late Quaternary period, and for its ability to reconstruct multiple paleoclimatic variables. In this paper, we aim to encourage not only greater use of pollen data for climate reconstructions, but also the better documentation of the underlying methodological choices, tools and datasets. To help in this process, we provide guidance on the appropriate application of the different techniques currently available, and the availability of the necessary tools and datasets required to apply these methodologies.
Better transparency and documentation will help both palynologists interested in reconstructing climate and also experts from other fields interested in how pollen reconstructions compare to other paleoclimatic proxies and earth system models. In the future, we hope to see greater use of the pollen-climate proxy outside of the traditional study regions of North America and Europe. In quantification deserts (Africa, Latin America, Southeast and Central Asia and Australasia, see Fig. 1 ), producing reliable reconstructions will require overcoming multiple challenges, not only scientific, such as the need for larger modern datasets, but also to help and encourage the scientific community to implement climate reconstruction techniques. As this and other data become more available, we can also expect to see key methodological developments in multi-sample and multi-site approaches that moves away from the traditional approach of treating each pollen sample as a single independent data point.
version of the modern dataset used. If the data are not publicly available, upload them on a public repository to DOI them. Definition of the study area (e. g. by countries and/or coordinates), Linkage of plant species to pollen taxa Report name and version of all the software, package or vegetation model used Dimension and shape of the pdfs, Selection of climate sensitive taxa, Minimum number of points to define a pdf, Weighting of the pdfs diversity, sampling context, etc. ) Deshrinking strategy, Number of components, Numbers of hidden neurons, Number of trees (if appropriate) MAT Dissimilarity metric, Number of analogues, dissimilarity threshold, Weighting of the analogues ANN, BRT, RF Structure of the neural networks or the trees, number of trees Bayesian Priors Inverse modelling Name and version of the PFT scheme Name and version of the vegetation model Number of input variables (4 or 6), Number of iterations, Dissimilarity metric.
master/Chevalier_etal_Review_Climate_Quantif_Pollen. The PCMIP (Pollen-Climate Methods Intercomparison Project) workshop was funded by an International Exploratory Workshop Grant from the Swiss National Science Foundation (SNF/FNS) (#IZ32Z0_173407). Participation of early career scientists at the workshop was assisted by travel grants from the International Union for Quaternary Research (INQUA) (Project no. 1705P) and the Past Global Changes organisation (PAGES). MC and PSS were supported by the SNF/FNS funded 'HORNET' Project (#200021_169598), KG and TL by Natural Sciences and Engineering Research Council of Canada (NSERC) Discovery Grants, KI by CLAVICHORD Project (H2020-MSCA-IF-EF, 705895), LNP by the FNS grant P2LAP2_187745, and MZ by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) Project number 2901391021 -SFB 1266.
The reactions were set up using RT HSPCR Mix SYBR® B (A&A Biotechnology) according to the manufacturer's specifications. LoROX dye was used as a passive reference. The expression level of the genes was normalized to the expression level of the actin gene (ACT1), used as internal calibrator. All the primers for RTqPCR were designed with Primer Expert Software (Applied Biosystems) and are listed in Table  S3.
Data processing and statistical analysis Concentration of the reporter proteins The absolute concentration of enzymatic reporters was calculated based on their specific activity values, equal to 80 [GAU mg -1 ] for TlG  (Basaveswara Rao et al. 1981)  and 478 [AAU mg -1 ] for SoA  (Baker and Woo 1985). The absolute concentration of eYFP reporter was calculated based on a calibration curve for eGFP provided by (BioTek Instruments, Inc. , Winooski, VT, USA). For the purpose of re-calculation, the following data were used to estimate the relative brightness of e Y F P w i t h r es p e c t t o e G F P : ε e Y F P = 83 , 4 0 0 [cm -1 M -1 ], Φ f eYFP = 0. 61, ε eGFP = 55,000 [cm -1 M -1 ], Φ f eGFP = 0. 60  (Wall et al. 2015).
Results Characteristics of the serotype 1 isolates. We compiled data for 909S. pneumoniae serotype 1 isolates originating from Sub-Saharan Africa and carried out a GWAS analysis to determine genetic differences between CNS and non-CNS isolates (Supplementary Data 1). Of these isolates, 297 were isolated from CSF in patients while 612 non-CNS isolates were isolated from the blood, lung aspirate, joint fluid, and pleural or peritoneal fluid (Fig. 1a ). All the CSF specimens were cultured from patients with meningitis or other CNS infections. The isolates were collected via hospital-based bacterial surveillance between 1996 and 2016. Age is an essential confounding factor associated with co-morbidities, immaturity and senescence of the immune system, which contributes to patient susceptibility to CNS invasion.