dataesr/scientific-paragraphs-categorization huggingface dataset:
Dataset descriptions To deliver information on these three research topics, we assembled eight distinct yet complementary pollen datasets. These datasets, described below and organized by topic, comprise extant material harvested from field and herbaria collections, and Pleistocene to Paleocene samples retrieved from rock outcrops. An overview of the datasets is provided in Table  1. Chemotaxonomic signals from sporopollenin. Dataset 1: Extant South American grasses. Pollen from 50 Poaceae species was either taken from the residue collection at the University of Amsterdam or sampled from the National Herbarium of the Netherlands at Naturalis Biodiversity Center, Leiden (Table  2 ; Jardine et al. 2020b, fig. S1 , table  S1 ). The pollen was extracted and the sporopollenin isolated using standard acetolysis for 6 min.
Diagenesis in non-exceptional settings. Dataset 2: Early Miocene to Pliocene grass pollen (~18 to ~5 Ma) from western Amazonia. The data come from nine samples from six localities in Colombia (Los Chorros and Santa Sofia), Peru (Madre de Dios, Pebas and Santa Teresa) and Venezuela (the Maracaibo Basin) (Jardine et al. 2020b, fig. S1 ). The pollen count data from the samples are documented in  Hoorn (1994 ), Vink (2012 ) and Berm udez et al. (2017) , which contain additional stratigraphic and locality information. All sample processing was carried out at the University of Amsterdam. Eight samples are clastics (very fine sands, silts and clays) and were processed using sodium pyrophosphate in a 10% solution with H 2 O (Na 4 P 2 O 7 Á10(H 2 0)), while the one lignite sample (sample 16788 from Los Chorros) was also oxidized with Schulze reagent (2HNO 3 , 60% : K 2 C10 3 , 7%).
For all samples the organic/inorganic fraction was separated with bromoform with density 2. 0 gs/cm 3. Dataset 3: Late Miocene to Pleistocene (5. 85-0. 54 Ma) grass pollen from the Amazon Fan. The data come from eight samples from a hydrocarbon exploration well (Well 2) situated on the upper slope of the continental shelf beyond the Amazon River delta (latitude 3°2 0 59″N, longitude 47°44 0 46″W; Jardine et al. 2020b, fig. S1 )  (Hoorn et al. 2017). The samples span a depth of 1644-3793 mbdf (metres below drill floor), which corresponds to a sediment depth of 865-3014 m once water depth (754 m) and the height of the drilling platform (25 m) are taken into account. The count data from the samples were published in  Hoorn et al. (2017)  and  Kirschner & Hoorn (2020). The samples were processed at the University of Amsterdam with hydrochloric acid (HCl), sodium pyrophosphate, acetolysis and bromoform separation  (Hoorn et al.
2017). Dataset 4: Late Pleistocene (520 to 13 ka) grass pollen from Lake Bosumtwi in Ghana. Count data from the majority of samples were published in  Miller & Gosling (2014) , and FTIR-derived UAC data from the upper 130 kyr of the Bosumtwi record were published in Jardine et al. (2016). The samples were processed at the Open University using HCl, HF, KOH and acetolysis, with eight samples processed both with and without acetolysis to study the impact of this process on sporopollenin chemistry  (Jardine et al. 2016). Generality across taxa. Dataset 5: Extant Nitraria pollen. Pollen from six different species, collected from various herbaria and collections. The sporopollenin was isolated using standard acetolysis. The FTIR data were previously published in  Woutersen et al. (2018) , which provides further information on the samples, as well as detailed morphological and chemical analyses of these species.
Dataset 6: Middle-late Eocene Nitraria pollen from the Tarim and Xining basins north of the Tibetan Plateau, which have been assigned to the form-genera Nitrariadites and Nitraripollis  (Hoorn et al. 2012). The Tarim sample (sample MS-BC22) comes from the late Eocene Bashibulake Formation collected at the Mine section, and was processed at Utrecht University with HCl and HF but without heavy liquid separation or oxidation  (Houben et al. 2011; Bosboom et al. 2014). The Xining sample (sample XJ88a) was taken from the middle Eocene Honggou Formation exposed at the Xiejia section, and processed at Palynological Laboratory Services Ltd, Holyhead, UK, with HCl, HF and heavy liquid separation. Further locality details are provided in  Meijer et al. (2019). Dataset 7: Extant conifer (Pinaceae and Podocarpaceae) pollen from Yunnan and Hunan provinces, China, collected in April to May 2018 directly from nine individual trees representing seven species (Jardine et al.
2020b, table  S2 ). The sporopollenin was isolated using standard acetolysis. Dataset 8: Paleocene fossil conifer pollen from the Nangqian Basin of east-central Tibet (NB unpub. data; see also  Horton et al. 2002). Samples are from eight form-species in six genera. These samples were processed at Palynological Laboratory Services Ltd with HCl, HF and heavy liquid separation.
Data generation Chemical data for the South American grasses, Nitraria and conifer pollen (Datasets 1-3, 5-8) were generated using a Thermo Scientific Nicolet iN10 MX Dual detector IR microscope at the University of Amsterdam. Atmospheric CO 2 and H 2 O variations were removed by purging the system with a dry nitrogen feed. Individual pollen grains were mounted onto ZnSe windows and analysed using 128 scans and a resolution of 4; in each case the microscope aperture was matched to the size of the pollen grain being analysed. A background spectrum was taken prior to each measurement and automatically removed from the sample spectrum. While we aimed for a target of 30-40 pollen grains per species, in some cases this was not possible due to a lack of material. Chemical data for the Lake Bosumtwi grass pollen (Dataset 4) were generated at the Open University, UK, using a Thermo Nicolet FTIR bench unit interfaced with a Continuum IR-enabled microscope, which was fitted with a 159 reflachromat objective lens and a liquid nitrogen-cooled MCT-A detector.
A purge unit was installed during data generation. Therefore, the uppermost 69 samples from the Bosumtwi sedimentary sequence were analysed with an unpurged system, while for the lower 62 samples the entire system was purged using a Peak Scientific ML85 purge unit. The lower 12 unpurged samples were also reanalysed with the purged system, to check the impact of the purging status on the results. To generate the data individual pollen grains were picked from the sediment samples and mounted on ZnSe windows in groups of 8-10 grains with 3 replicate groups per sample. FTIR spectra were generated using with a microscope aperture of 100 9 100 lm, 256 scans per sample and a resolution of 4. For further details see  Jardine et al. (2016).
2009; Zimmermann et al. 2015a Zimmermann et al. , 2016;; Liland et al. 2016). Non-differentiated spectra were preprocessed with an EMSC model incorporating third order polynomial effects, whereas differentiated spectra were preprocessed with an EMSC model incorporating first order polynomial effects. EMSC allows for a reference spectrum to be incorporated to guide the baseline correction  (Zimmermann et al. 2015a; Liland et al. 2016 ). Here we followed standard practice and used the mean spectrum for each dataset as the EMSC reference spectrum, except for the Nitraria and Lake Bosumtwi spectra (Datasets 4-6) which all showed a pronounced baseline shift. Therefore, the baseline corrected mean spectrum for each of the three datasets was used as the EMSC reference spectra. For the Nitraria datasets we used a second-order polynomial baseline, and for the Bosumtwi data a first-order polynomial baseline (i.
e. a straight line). We tested classification potential using k-nn classification on the extant grass spectra (Dataset 1). Unknown objects (here IR spectra) are classified based on the identities of the most similar objects in the training set, with similarity in this case being the lowest Euclidean distance. The parameter k is the number of most similar objects (nearest neighbours) used for classification, and based on past research  (Jardine et al. 2019)  this was kept to k = 1 because increasing k beyond this does not improve classification success. To provide a thorough test of classification success we divided the dataset into a training set (2/3) and a validation set (1/3) by holding back every third sample for validation. We then used the training set to select the best combination of processing parameters (i.
Chemotaxonomic signals from sporopollenin The mean FTIR spectrum for each grass subfamily (Dataset 1) shows absorbance bands associated with carboxyl groups at 1710 cm À1 (C=O stretching), and aromatic compounds at 1590 cm À1 (C=C stretching), 1430 cm À1 and 1370 cm À1 (C-H bending) (Fig. 1 ). There are prominent peaks at 1170 and 1030 cm À1 , with the 1170 cm À1 peak having a shoulder at 1230 cm À1. While peaks at or close to these wavenumbers are present in the phenolic compounds paracoumaric and ferulic acid that form part of the sporopollenin biopolymer (Ba gcio glu et al. 2015), similar peaks have also been interpreted as new C-O bonds forming during acetolysis  (Dom ınguez et al. 1998). The small peak at 1650 cm À1 is the amide I peak (C=O stretching), and relates to proteins external to the pollen wall that were not fully removed during acetolysis  (Jardine et al.
2015 (Jardine et al. , 2017)). While the spectra of the different grass subfamilies are consistent in the peaks present, there are subtle variations in both peak height and position. The maximum subfamily classification success rate with the training dataset was 97%, with SG smoothed second derivative spectra and w = 25 (Table  3 ). The next best combination of parameters was with SG smoothed first derivative spectra (96%), and then unprocessed spectra (90%). The genus level classification success rate was 5-10% below the subfamily rate (Table  3 ). Processing the validation dataset with second derivatives and classifying the spectra based on the identities of the training dataset gave a classification success rate of 98% at the subfamily level, and 93% at the genus level. Confusion matrices for the training and validation datasets both show a similar pattern of misclassifications at the subfamily level, but without any obvious phylogenetic signal (i.
Diagenesis in non-exceptional settings There are substantial chemical differences between the extant (Dataset 1) and fossil (Datasets 2 and 3) South American grass pollen grains (Fig. 4 ). A PCA of these data divides the extant and fossil data on PC 1, with some additional separation between the Amazon Fan and western Amazonia spectra (Fig. 4A ). Both the mean spectrum from each dataset (Fig. 4B ) and the PC 1 loadings (Fig. 4C ) show that the fossil spectra have relatively higher 1600 and 1370 cm À1 aromatic peaks, and a much reduced 1710 cm À1 carboxyl peak. Additional differences occur in the lower wavenumbers and probably relate at least in part to laboratory processing procedures: while all spectra have a peak at ~1030 cm À1 , this is more pronounced in the extant grains and also to some extent in the Amazon Fan spectra, all of which were acetolysed.
There are some differences detectable among localities in the western Amazonia fossil spectra (Dataset 2), with samples from Santa Sofia having a more pronounced 1370 cm À1 peak and samples from Los Chorros having higher peaks at ~1040 and 1115 cm À1 (Fig. 6A ). Multiple samples from the same locality have highly similar spectra, which is also the case for Los Chorros despite these samples representing different lithologies and sample processing methods (sample 16835 is clastic that was processed with sodium pyrophosphate, while sample 16788 is a lignite that was processed with both sodium pyrophosphate and Schultze reagent; see Material and Method, above, for further details). A PCA shows grouping of spectra within localities, but no consistent changes with time (Fig. 6B ). Similarly, there are differences among samples in the Amazon Fan fossil spectra (Dataset 3) that leads to within-sample grouping in a PCA, but no obvious changes through time (Fig.
7 ). The Lake Bosumtwi grasses (Dataset 4) show limited evidence for consistent chemical changes over the last 500 kyr. The first two axes of a PCA, which together account for 56% of the variance in the dataset, show no obvious temporal trends, apart from a possible slight increase in PC 2 scores at ~350 ka BP (Fig. 8A ). Higher principal components also show no clear change through time (Jardine et al. 2020b, fig. S8 ). The main chemical shift occurs ~50 to 90 ka BP with a transient increase in PC 1 scores, driven by increases and decreases in the relative heights of peaks and troughs across the spectra (Fig. 8B ). There are no clear differences associated with whether the FTIR system was purged or unpurged F I G. 1. Stacked mean FTIR spectra for each subfamily in the extant South American grass dataset.
Subfamily colours are the same as those in Figure  2A , and shaded regions show AE1 standard deviation about the mean. Key peaks are marked with dashed grey lines. (Jardine et al. 2020b, fig. S8 ). Comparing acetolysed and non-acetolysed spectra reveals the presence of peaks at 1170 and 1030 cm À1 in the acetolysed spectra, and a more pronounced 1710 cm À1 peak in the non-acetolysed spectra, as well as a range of smaller peaks in the 1250 to 1450 cm À1 range (Fig. 8C ). The acetolysed Bosumtwi spectra are similar to those of the extant South American grasses (Dataset 1; Figs 1, 4B), with the major differences being the lack of protein-related peaks in the Bosumtwi spectra, which suggests the full removal of these during burial and/or processing. The non-acetolysed Bosumtwi spectra are distinct from the (also non-acetolysed) western Amazonia fossil spectra (Dataset 2; Fig.
Generality across taxa Both the Nitraria (Datasets 5 and 6) and conifer (Datasets 7 and 8) pollen show large differences between the extant and fossil spectra (Fig. 9 ), with similar changes to those in the South American grasses (Datasets 1-3; Fig. 4B ). While the spectra for the extant taxa are broadly similar, the Palaeogene Nitraria and conifer pollen spectra are simpler than the Miocene to Pleistocene South American grass spectra, with less clearly defined peaks. The fossil Nitraria spectra are broadly similar across the two samples and basins (Tarim vs Xining), but show some differences in the 1000 to 1100 cm À1 range (Fig. 9A ).
DISCUSSION Our results demonstrate a mix of promising aspects and challenges for sporopollenin chemistry-based palynology. The high classification success rate demonstrated by the extant grass taxa (Dataset 1, 98% at subfamily level for the validation dataset) indicates that sporomorph chemotaxonomy can be applied effectively with isolated sporopollenin, including with acetolysed grains and larger numbers of taxa. Even reliably classifying grass pollen grains to subfamily level represents a major advance from what is possible with standard light microscopy, and our genus level results suggest that classifying to finer taxonomic levels may also be achievable  (Julier et al. 2016). Our results also suggest that the photosynthetic pathways of plants may be identifiable via FTIR analysis of sporomorphs (Fig.
3B ), and this would provide a new and efficient means of quantifying the relative abundance of C 3 versus C 4 grasses through time  (Amundson et al. 1997; Loader & Hemming 2000; Nelson et al. 2006 Nelson et al. , 2008)). Nevertheless, we stress that further replication is needed across taxonomic scales, including multiple individuals within species, to gain a fuller understanding of likely classification success at genus and species level, and better assess the potential for distinguishing among C 3 and C 4 grasses. The consistency of the signal through time in the Lake Bosumtwi grass pollen spectra (Dataset 4, Fig. 8 ), and the broad chemical similarity with the extant South American grasses (Fig. 4C ), suggests that chemotaxonomy using extant calibration datasets has potential in late Quaternary settings.
The late Quaternary timeframe encompasses questions regarding vegetation changes across glacial-interglacial cycles  (Torres et al. 2005 (Torres et al. , 2013;; Melles et al. 2012; Miller & Gosling 2014; Miller et al. 2016) , human impacts on natural systems  (Bush 2002; Bush et al. 2015; Loughlin et al. 2018) , and the spread of agriculture  (Piperno et al. 2004; Weiss et al. 2004; Fuller 2007; Crowther et al. 2016; Jardine et al. 2019) , which could be addressed using a chemotaxonomic approach. In addition to increasing the taxonomic resolution of morphologically cryptic taxa (such as grasses), chemotaxonomy may allow for automated pollen counting, which is a long-standing goal in palynology  (Holt et al. 2011; Holt & Bennett 2014). In spite of the promising results demonstrated by the extant and late Quaternary datasets, considerable differences occur between the spectra of extant and fossil pollen.
These differences are caused partly by the laboratory processing methods used, and partly by post-burial processes that result in the transformation of biological material into geological/palaeobotanical samples. Comparison of the acetolysed and non-acetolysed grass pollen from Lake Bosumtwi (Dataset 4), in which sample age and processing are otherwise identical, reveals a number of differences across the spectra (Fig. 8C ). Encouragingly, the non-acetolysed spectra are highly similar to published FTIR spectra of sporopollenin that has been chemically isolated for microencapsulation, for example the 'high temperature switched treatment' of Gonzalez-Cruz et al. (2018; see their fig. 7 ). This method employs treatments with acetone (overnight), orthophosphoric acid (7 days) and KOH (12 h), with interim washes with water, acetone, HCl, NaOH and ethanol, to isolate sporopollenin.
8C ). Otherwise, if the samples to be compared are all extant or late Quaternary, it will be necessary to ensure that all have undergone the same chemical treatments to make them fully comparable. Interestingly, the effect of acetolysis was less pronounced in the fossil material. Comparing the Miocene to Pliocene dataset from western Amazonia (Dataset 2, not acetolysed) with the Miocene to Pleistocene Amazon Fan dataset (Dataset 3, acetolysed) reveals broadly similar spectra (Fig. 4C ). The additional peaks that are present in the extant and late Quaternary acetolysed material are only subtly expressed in the Amazon Fan spectra. As yet, it is not clear whether this is down to different base materials (i. e. extant sporopollenin vs sporopollenin that has undergone a degree of burial across longer geological timescales) or to additional steps in the processing of the Amazon Fan samples that could have removed the impact of acetolysis.
Beyond acetolysis, the other processing reagents used in this study may have led to inter-dataset differences. While the extant datasets were simply acetolysed, the fossil datasets were variously treated with KOH, HF, HCl, sodium pyrophosphate, Schulze reagent and heavy liquid separation, as well as acetolysis in some cases (Table  1 ). KOH has been shown to have a minimal impact on sporopollenin chemistry (Lutzke et al. 2020), and the same is thought to be true for HF  (Dom ınguez et al. 1998)  but the effect of the other reagents has not been explicitly tested. The Bosumtwi samples (Dataset 4) were treated with HF, HCl and KOH, and the overall similarity of the extant (Figs  1, 4 ) and acetolysed Bosumtwi (Fig. 8C ) spectra suggests that the impact of these reagents on sporopollenin chemistry is likely to be limited.
This is supported by the similarity between the non-acetolysed Bosumtwi spectra (Fig. 8C ) and published FTIR spectra from sporopollenin isolated for pharmaceutical microencapsulation applications (see previous discussion and Gonzalez-Cruz et al. 2018). If correct, this suggests that the HF and HCl used in processing the fossil Nitraria (Dataset 6) and conifer (Dataset 8) pollen should also have caused minimal changes to the sporopollenin chemistry, although we cannot rule out subtle responses to these reagents. The two samples from the Miocene Los Chorros locality in western Amazonia (Dataset 2) represent two different lithologies (clastic and lignite) and processing approaches: while both samples were processed with sodium pyrophosphate and bromoform heavy liquid separation, the lignite sample (sample 16788) had an additional processing step with the use of Schulze reagent.
This loss is not recreated with acetolysis or burial over short-timescales (i. e. 10 5 years) and/or at shallow depths (150 m for the F I G. 9. Mean FTIR spectra for extant and fossil Nitraria and conifer pollen, with shaded regions showing AE1 standard deviation about the mean. A, Nitraria. B, conifers. oldest Lake Bosumtwi samples, vs 865 m for the sample of a similar age from the Amazon Fan). Our results suggest that this repolymerization can happen relatively early once sporomorphs enter the geological record, but do not rule out the possibility that the resultant geopolymer can be altered by further diagenetic processes. Such an effect may account for the continued simplification of the spectra shown by the Palaeogene Nitraria and conifer pollen (Datasets 6 and 8; Fig. 9 ) compared to the Neogene grasses (Datasets 2 and 3; Fig.
4 ). These results also suggest that exceptional preservation of the kind reported by Fraser et al. (  2012 ) is likely to be rare in the fossil record, with some level of chemical change to be anticipated even over short geological timescales. Similar changes (i. e. the loss carboxylic acids and a concomitant reduction in the 1710 cm À1 C=O peak) have been recreated in thermal maturation experiments  (Yule et al. 2000; Watson et al. 2012; Bernard et al. 2015) , suggesting that such experiments are successfully simulating early diagenetic processes and the repolymerization of the labile sporopollenin fraction. It is not clear, however, how well they can recreate longer term changes following the formation of the sporopollenin geopolymer. Nevertheless, a similar approach with extant material is likely to provide a better match for deep time fossil specimens than is possible with acetolysis (or other laboratory processing) alone, which may enable the development of calibration datasets from extant samples that can be used with fossil material.
Using the chemical signature of fossil specimens with known affinities may also be viable as a means of classifying problematic fossil material. Such as approach has been used with Silurian cryptospores to demonstrate their relationship with land plants rather than marine palynomorph groups  (Steemans et al. 2010) , and with fossil cuticle fragments with ambiguous phylogenetic relationships  (Vajda et al. 2017). Therefore, as long as a taxonomic signature is still present in deep time sporopollenin (or cuticular material), there is potential to use it for classification purposes even if it is modified from its original state. We have taken a broad-scale exploratory approach with this study, analysing different datasets that incorporate a range of taxa, time periods and laboratory processing procedures.
This has enabled us to consider a number of variables that may impact upon sporopollenin chemistry, using samples that are representative of the majority of routine palynological investigations and existing sample sets. However, it also means that separating out the impact of these different variables, and understanding how they act individually and in combination with each other, is more challenging than in a manipulative experimental setting. Further work is therefore needed to test and validate the results presented in this paper. This research should be targeted towards dedicated experimental approaches where processing procedures can be harmonized across extant and fossil datasets, and the potential controlling factors can be manipulated and tested independently of each other. This will be particularly useful for disentangling the impact(s) of different processing reagents, how they may interact in processing protocols, and whether and how they act differently on sporopollenin from extant sporomorphs vs fossil sporopollenin of different diagenetic grades.