dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This chapter has its origins in the Damlien H (2014)  Eastern pioneers in westernmost territories? Current perspectives on Mesolithic huntergatherer large-scale interaction and migration within Northern Eurasia. Quat Int   419:5-16 Damlien H  (2016) Between tradition and adaptation. Long-term  trajectories of lithic tool-making in South Norway during the postglacial colonization and its aftermath  (c. 9500-7500 cal BC). Unpublished PhD thesis,  University of Stavanger ,  Stavanger De Geer  G (1888) Om Skandinaviens nivåforandringar under Quartorperioden. Geologiska Föreningens i Stockholm Forhandlingar , 10:366-379, 12:6-110 Eggen I, Nymoen P, Hammer Ø, Sjöling, E (2014) Excavation report and analyses from Hummervikholmen, Hallandvik,  Søgne k , Vest-Agder. Museum of Cultural History, University of Oslo Fischer A  (ed) (1995) Man and sea in the Mesolithic, Oxbow Monographs 53.
Our appreciation goes to INSTAP for the support of the fieldwork on Cyprus. Acknowledgments We wish to thank the EU through COST Action TD0902 SPLASHCOS, the National Geographic Research Foundation, CARE Archaeological Foundation, MAFCAF Foundation, the Dan David Foundation, The Shelby White and Leon Levy Program for Archaeological Publications and Sandy and Joseph Lepelstat for their financial support of the research described here. The Israel Antiquities Authority, the Israel Prehistoric Society and Haifa University are acknowledged for their financial and administrative support of the underwater excavations and H. Rosenstein and Fantasea for providing photographic equipment. We also wish to express our appreciation to the divers and archaeologists who participated in the underwater excavations, to the many researchers who subsequently contributed to the study of the finds, to S.
9 Belgium: Prehistoric and Protohistoric Archaeology in the Intertidal and Subtidal Zones… past two decades scientific and management skills and expertise in underwater cultural heritage including the intertidal zone. Together with the Flanders Marine Institute, the Flanders Heritage Agency also hosts an online database, <www. maritime-archaeology. be>, on maritime archaeological heritage. Thanks to a new research mandate, the Flanders Marine Institute will be able in the near future to perform, coordinate and organize new and complementary research related to underwater cultural heritage, in close collaboration with academic institutions. Under the sea: archaeology and palaeolandscapes of eeuwse en een vroeg 18de-eeuwse belegering van the continental shelf. Springer, Cham, pp 415-424 Oostende, Relicta Monografieën 8, Brussels Mol D (1991) Het ijstijdlandschap van de zuidelijke Pieters M, De Clercq W, Demerre I, Missiaen T, Van Noordzee.
viewer. eu/, and this in turn is based on a variety of online records. For England, records of archaeological sites are maintained by Historic England, and the database is currently known as the National Record of Historic England, NRHE (formerly the National Monuments Record, NMR) (https://www. pastscape. org. uk/). Historic England also maintains the Intertidal and Coastal Peat Database (https://historicengland. org. uk/research/current/heritage-science/intertidal-peat-database/) and the Rapid Coastal Zone Assessments (e. g. https://historicengland. org. uk/images-books/publications/nwrcza/). In Wales, the equivalent is the National Monuments Record of Wales (NMRW) maintained by The Royal Commission on the Ancient and Historical Monuments of Wales (http://www. coflein. gov. uk/), and in Scotland, Canmore (https://canmore.
org. uk/), maintained by Historic Environment Scotland. Note that these are the current names and websites of these government agencies but that both are liable to change on a regular basis. The Archaeological Data Service (ADS) (http://archaeologydataservice. ac. uk/) also provides a permanent archive of some of these records and useful links to other relevant reports such as Wessex Archaeology's (2007) 'Artefacts from the Sea' database. All these sources have been consulted in compiling data for the SPLASHCOS Viewer 10 Great Britain: The Intertidal and Underwater Archaeology of Britain's Submerged Landscapes the same time, it is likely that our reduced list is probably a minimum figure. Where intensive investigation has been carried out, for example along the 6-km stretch of shoreline at Wootton Quarr on the north coast of the Isle of Wight, there are multiple occurrences of stone tools, ceramics, wooden trackways and other cultural features, amounting in total to 180 occurrences covering a time range from the late Mesolithic through to the Medieval period (Tomalin et al.
Motivation: The proteomics field requires the production and publication of reliable mass spectrometry-based identification and quantification results. Although many tools or algorithms exist, very few consider the importance of combining, in a unique software environment, efficient processing algorithms and a data management system to process and curate hundreds of datasets associated with a single proteomics study. Results: Here, we present Proline, a robust software suite for analysis of MS-based proteomics data, which collects, processes and allows visualization and publication of proteomics datasets. We illustrate its ease of use for various steps in the validation and quantification workflow, its data curation capabilities and its computational efficiency. The DDA label-free quantification workflow efficiency was assessed by comparing results obtained with Proline to those obtained with a widely used software using a spiked-in sample.
These algorithms may introduce inaccuracies and errors throughout the data-processing pipeline. As a result, there is room for improvement in the different steps in identification and quantification pipelines; result reliability must also be carefully assessed, not only by statistically controlled procedures, but also through examination of the underlying data by experts. Thus, although comprehensive and reliable results should be produced through an automated process, it is also very important to be able to manually verify, validate and curate erroneous identification and/or quantification results using a dedicated graphical user interface. Proteomics experiments are moving toward more complex and ambitious studies, not only based on the analysis of a single fractionated sample, but also composed of multiple datasets with hundreds of samples providing a broad understanding of a biological system from a variety of viewpoints  (Aebersold and Mann, 2016).
However, despite recent improvements in the field, through the development of free software algorithms and tools to tackle this level of complexity, there is still a need for a laboratory information management system dedicated to MS-based proteomics results that could implement the core data-processing steps (validation, quantification, visualization and submission to repositories), and at the same time could provide an organized and sustainable data persistence system allowing users to explore, compare and automatically or manually validate multiple datasets. With all these different goals in mind, we developed Proline, a production grade software suite, which provides a unique environment for large-scale MS data management, visualization, analysis and curation with the main objective of promoting the production and sharing of high-quality proteomic datasets.
Proline can be used (i) to produce reliable identification and quantification results through robust automated processes, (ii) for data curation, (iii) to systematically save and keep track of metadata from processing steps, parameters and generated data and (iv) to submit highly qualified datasets to public repositories  (Vizcaı ´no et al. , 2009 (Vizcaı ´no et al. , , 2014)). Altogether, these features make Proline perfectly fitted to the needs of proteomics core facilities and research labs producing a huge amount of data at high throughput.
Materials and methods In the current version of Proline, the standard workflow is mainly focused on validation of peptide and protein identifications, and label-free quantification of those peptides and proteins based on spectral counting or MS1 peak ion intensity. A workflow in Proline is implemented as a collection of tasks (see Fig. 1 ) that can be performed by the user through the graphical user interface (Supplementary Fig. S1 ). The more important tasks are detailed in the following paragraphs. In a classical case, users can import multiple identification results corresponding, e. g. to fractions and replicates of a biological sample and combine them before or after validation. The resulting datasets can then be compared or quantified using spectral counting or data dependent acquisition (DDA) label-free quantification, before exporting the results in different file formats.
Import Identification results files produced by several search engines (Mascot, X! Tandem, OMSSA and Andromeda) can be imported into Proline in their native format. In addition, the mzIdentML format is supported to allow the output from any other search engine compatible with this standard to be imported (e. g. MS-GFþ). During this step, no filtering or thresholding is applied: along with the search parameters, all submitted spectra, peptide spectrum matches (PSMs) and protein hits suggested by the search engine are retained in the Proline database to allow subsequent validation of putative identifications. Imported data are organized into search results that can be browsed in Proline's graphical user interfaces before validation.
Each set of peptides is finally associated to one or multiple indistinguishable FASTA entries (a. k. a. protein sets). Note that this classification relies only on sequence comparison and that PTM information (such as N-terminal modifications) is not taken into consideration. In Proline, the whole list of validated PSMs and their corresponding protein sets is called an identification summary. Each summary is persisted in the Proline database for immediate visual inspection. The summary also serves as a checkpoint for subsequent data processing, such as combination of multiple identification summaries into a single dataset, or quantification of the identified and validated species.
Combining identifications In Proline, identification results can be combined to construct a parent dataset, and create a non-redundant list of identified peptides and proteins. This combination can be performed either before validation (on search results) or after validation (on identification summaries). Since this operation could be recursively performed, it leads to hierarchical structuring of search results and/or identification summaries. On the one hand, combination before validation (taking into account all PSMs identified by the search engine) may, e. g. be relevant when analyzing results obtained after peptide fractionation: in that case, Proline provides a set of predefined tasks (dark blue) that can be executed and the paths linking the tasks defines analysis workflows several peptides belonging to the same protein may be spread across different result sets; these sets should be merged before protein validation.
On the other hand, merging identification summaries is appropriate when seeking to group the validated results from series of individual samples to be compared or when combining data from different search engines. As Proline does not rank peptides identified using different search engines, unlike other types of software which compute a metascore  (Shteynberg et al. , 2013; Vaudel et al. , 2015) , search results from different search engines should not be combined before validation. Except in this latter case, either combining datasets before or after validation could be used depending on the user's needs. In both cases, for a given peptide, all PSMs originating from the initial datasets can be conserved ('union' mode), or only the PSM of highest score among all the combined search results can be retained  ('aggregation' mode).
Spectral counting Protein sets from different identification datasets can be compared in Proline by counting MS/MS spectra at peptide and protein set levels. Three different spectral counting metrics are calculated for each protein set: the first one considers all identified peptides, the second one examines only specific peptides (peptides identifying a single protein set), and the third takes shared peptides into account  (Hesse et al. , 2016). In the latter case, shared spectral occurrences are distributed across protein sets based on a weighting factor calculated using the proportion of unique peptides associated with each protein set.
MS1 peptide quantification More accurate comparisons can also be made by quantifying MS1 signals. Proline detects chromatographic peaks from raw data converted to the mzDB format  (Bouyssieet al. , 2015). The converter (https://github. com/mzdb/pwiz-mzdb) is based on ProteoWizard, ensuring a compatibility with a wide range of instrument vendors. The following list of file formats can be used as input: Thermo Raw files, AB Sciex Wiff files, Bruker Baf files and mzML files. After a first signal detection step (see Supplementary Section 'Signal extraction from mzDB files'), the algorithm associates the chromatographic peaks detected with validated PSMs, first by retrieving the corresponding MS/MS spectra acquired during the peptide elution (i. e. within the detected chromatogram boundaries), and then by matching the precursor m/z value of these spectra to the chromatographic peak m/z value (see Supplementary Section 'PSM assignment and deisotoping').
After the deisotoping step, the abundance of each ion is estimated from the apex of the chromatographic peak, which corresponds to the theoretically most abundant isotopolog (inferred from the peptide's atomic composition). The software then aligns the retention time of these annotated ions for all the LC-MS runs to be compared, and uses this information to crossassign MS/MS data to ions (i. e. chromatographic peaks) that were detected but not identified in other runs (see Supplementary Section 'Cross-assignment'). The resulting ion abundances are finally stored in the Proline database, making them available for rapid data visualization and further post-processing.
Experimental datasets The dataset used to evaluate the software was prepared by spiking, respectively, 0. 01-0. 05-0. 1-0. 250-0. 5-1-5-10-25-50 fmol of the UPS1 equimolar mix (Sigma) into 1 lg of yeast lysate (Merck). Trypsin digested samples were then analyzed in quadruplicate by nanoLC-MS/MS using a nanoRS UHPLC system coupled to a Q-Exactive Plus mass spectrometer, to produce 40 raw MS files. A detailed description of the parameters used for data processing with each bioinformatic workflow (Mascot-Proline and Andromeda-MaxQuant) can be found in the Supplementary Material.
Results and discussion To assess the efficiency of Proline for the relative label-free quantification of proteomes based on intensity feature extraction, we compared its performance to that of MaxQuant  (Cox and Mann, 2008) , a widely used state-of-the-art tool. The comparison was based on a standard ('ground truth') dataset generated from a yeast lysate in which 10 different levels of the equimolar UPS1 mix of 48 human proteins were spiked, as described elsewhere  (Ramus et al. , 2016). Signals for yeast proteins are expected to be constant across all samples, whereas UPS1 signals should vary between samples. Proline was associated with the Mascot search engine for database search, and we thus globally compared the results of the Mascot-Proline workflow to that of Andromeda-MaxQuant. In the first place, we found that these workflows behaved similarly in terms of protein identification and validation, with approximately the same numbers of UPS1 and yeast proteins identified and validated in each case (Supplementary Fig.
Indeed, the signals that must be cross-assigned between runs may be very close in m/z and/or time dimensions to signals produced by other peptide species. Consequently, the algorithms used must be able to avoid extraction of abundance by cross-assignment for a PSM present in one sample but absent from another. To achieve this requires, in particular, a thorough retention time alignment between runs to be compared with compensate for reproducibility issues during the chromatographic separation step. We used our standard dataset (see Section 2) to assess the efficiency of Proline's cross-assignment procedure. To that aim, we compared the proportion of MVs in the quantification results obtained for yeast peptide ions from the 40 MS runs composing the dataset using both Proline and MaxQuant. When the crossassignment procedure was not used, the number of MVs across runs mainly reflects the imperfect reproducibility of the LC-MS workflow, notably due to the limited sampling ability of MS instruments, as well as limits of the successive algorithms applied to identify and extract the peptide abundances.
This value is representative of the accuracy of the label-free measurement. Conversely, the lower panel in Figure  2B  shows the CVs for ions after crossassignment. The distributions of CVs calculated for these ions before and after cross-assignment remain very similar, with a median CV shifting only from 14. 6% to 16% using Proline (15. 2-17. 6% using Maxquant), indicating that the process probably does not induce major assignment errors nor increase the variability of the final number of matched peptide ion abundances. This conclusion remains unchanged when CVs are calculated from median normalized abundances. In conclusion, the cross-assignment procedure can be used to reduce the proportion of MVs by a factor of 4 using Proline (versus a factor of 2. 3 using MaxQuant). A higher number of ion values were recovered by Proline for this dataset.
Accuracy of protein abundance measurements To investigate the accuracy of the protein abundances measured by Proline we used the same dataset, but focused our attention on the spiked UPS1 proteins, for which the theoretical relative abundances in the different samples were known. We first used a simple sum of peptide abundances obtained by summing ion abundances to infer protein abundances for each of the different concentrations of UPS1 proteins. The comparison of measured and theoretical ratios between the highest UPS1 concentration (50 fmol/mg of yeast lysate) and the lower concentration spikes revealed that for most UPS1 proteins, both Proline and MaxQuant accurately estimated the ratios for concentrations down to 1 fmol/mg  (Fig. 3, upper panel). For lower concentration points, for which some peptides were below the detection limit of the mass spectrometer, the two software behaved differently.
3 , bottom right panel). As illustrated on individual plots for the 48 UPS1 proteins (Supplementary Fig. S8 ), this dispersion results from MaxQuant's tendency to overestimate the ratio for a subset of proteins, i. e. to underestimate the abundance of proteins spiked at low concentrations, possibly because it has missed some low-intensity signals. Conversely, more low-abundance peptide signals were generally extracted by Proline, providing a more accurate estimation of the ratios down to a spiked   S9 ), the software can indeed retrieve 'true' peptide signals that are detectable down to 250 amol/mg. Altogether, these results demonstrate that the signal extraction procedure in Proline is highly sensitive, and thus the number of MVs is reduced and the accuracy of protein quantification at low concentration is improved compared with MaxQuant.
MS1 quantification The ultimate goal of most quantitative proteomics studies is to find proteins whose relative abundance is significantly increased or decreased in complex samples. Among the numerous samples or datasets allowing to assess the performance of quantitation workflows, the one provided to volunteer participants of the 2015 study  (Choi et al. , 2017)  by the Association of Biomolecular Resource Facilities is interesting since it gives the possibility to compare Proline's performances with the 51 submissions reported (see Supplementary Section 'MS1 quantification'). However, to enable an accurate comparison of performances a sufficiently large number of differentially abundant proteins are required. Similar to what was described in  (Ramus et al. , 2016)  we gathered together the quantitative results of three pairwise comparisons of four different spiked levels of UPS1 in a yeast lysate (50 versus 25 fmol/mg, 50 versus 5 fmol/mg and 50 fmol/mg versus 500 amol/mg), to obtain differentially abundant proteins at various concentrations (expected fold changes of respectively 2, 10 and 100).
Both software successfully discriminated UPS1 proteins with a theoretical fold change of 10 and 100 from the yeast background, either based on fold change or P-value, even when only the simple sum aggregation method was used  (Fig. 4, upper panels). The separation of the expected variant and invariant populations was less obvious in the 50 versus 25 fmol/ mg comparison when using sum aggregation, but became more so when the ratio-fitting method was applied. This enhancement was mainly due to improvement of the P-values for expected variant proteins (Fig. 4 , compare upper and lower panels). Furthermore, application of the MRF or MaxLFQ methods had a similar impact on the quantification of invariant yeast proteins (black circles) but to a more limited extent on the P-value axis when using Proline.
Importantly, the fold changes measured for UPS1 proteins were closer to the expected values with Proline than with MaxQuant, whether the corresponding ratio-fitting algorithm was used or not, especially for higher theoretical fold change (Fig. 4 ). The q-values were then used to classify variant and invariant proteins from this mixed dataset, and performance was assessed using receiver operating characteristic curves (ROC curves, Fig. 5 ). Results obtained by the most straightforward aggregation approach (sum aggregation, solid lines) demonstrated that Proline retrieves a high proportion of true positive (TP) UPS1 proteins while maintaining a low rate of false positive (FP) yeast proteins. Interestingly, although the number of proteins identified and quantified by the Mascot-Proline workflow was slightly higher, the number of FP was always lower in Proline results than in MaxQuant results (e.
g. as shown in Supplementary Table  S3 , a q-value < 10 À3 generated 26 FP in Proline versus 44 in Maxquant). These results corroborate the observed higher precision and accuracy of signal measurements and the lower rate of MVs returned by Proline. The use of ratio-fitting algorithms to aggregate peptide abundances-MRF and MaxLFQ in Proline and MaxQuant, respectively-allows better discrimination of expected variant and invariant proteins (dashed lines in Fig. 5 ). Indeed, this approach lowers the q-values obtained, especially for differentially abundant proteins, since it is less sensitive to outlier peptides than a simple sum aggregation. With our test dataset, a better balance between true positive rate (TPR) and false discovery proportion (FDP) was obtained using MRF in Proline (from 99% TPR, 28% FDP to 100% TPR and 20% FDP at a q-value threshold < 10 À2 ).
Expected linearity Median of 48 proteins raƟo  Fig. 3. Estimated versus expected ratios for UPS1 proteins. The abundances of the 48 UPS1 proteins were extracted by Proline (left panel) and MaxQuant (right panel) in each sample from the standard dataset, using either a sum aggregation (upper panels) or ratio-fitting algorithms (bottom panels). The ratios determined, calculated relative to the 50 fmol/mg concentration, were plotted against the expected ratios for the UPS1 proteins across the 10 different concentration points. Both Proline and MaxQuant accurately estimated the ratios calculated for concentration spikes down to 1 fmol/mg (expected ratio 5:6) for most UPS1 proteins. For lower concentration points, when some peptides fell below their limit of detection, the two software behaved differently, with a trend for overestimation of the ratio for MaxQuant, while Proline ratios were still well-fitted down to 250 amol/mg (expected ratio 7:6).