dataesr/scientific-paragraphs-categorization huggingface dataset:
𝑘 ∈ ItVars (Iteration variables) 𝜋 ::= ℓ. 𝜋 | ℓ 𝑘. 𝜋 | 𝜑 𝑘 | 𝜀 (Path) 𝑚 𝜋 ::= 𝜋 → 𝜋 ′ (Memory Move) In the rest, we freely use properties of regular languages on paths. The Atomize function aims to decompose subtree movements (where the iteration is implicit) into memory movements with explicit iteration. On the way, it eliminates spurious "self-conflict", i. e. , rules whose source and destinations are in conflict and reveal potential parallelism for later phases. We first look at the output of Atomize on an example. Example 3. In Example 2, we obtained the followind subterm moves for the first clause of Example 1: 𝑗 : 𝑖𝑛𝑡 |. 2/𝑡𝑟𝑒𝑒. 1/𝑖𝑛𝑡 →. 1/𝑖𝑛𝑡 𝑖 : 𝑖𝑛𝑡 |. 1/𝑖𝑛𝑡 → ∅ 𝑏 : 𝑡𝑟𝑒𝑒 |. 2/𝑡𝑟𝑒𝑒. 0/𝑡𝑟𝑒𝑒 →. 0/𝑡𝑟𝑒𝑒 𝑎 : 𝑡𝑟𝑒𝑒 |. 0/𝑡𝑟𝑒𝑒 → ∅ 𝑐 : 𝑡𝑟𝑒𝑒 |. 2/𝑡𝑟𝑒𝑒. 2/𝑡𝑟𝑒𝑒 →. 2/𝑡𝑟𝑒𝑒 The tree can be further decomposed into independant layers (eg.
Code emission The previous section provides us with a schedule  (, ì ) for each move of a given clause, the objective is now to generate a sequence of loop nests that will compute each (set of) moves in the order specified by the schedule, without forgetting any subcomputation. We use an algorithm inspired by previous works on code generation for the polyhedral framework  [3, 17]  depicted in Algorithm 4. It contains an inner procedure LoopGen which iterates recursively over the polyhedra to create a tree of nested loops. At recursive call , LoopGen generates the sequence of loops corresponding to dimension  of the schedule. At this point, we collect the projection of the polyhedra along dimension  which we partition and merge with the procedure MergePolyhedra. This gives us a list of polyhedra which delimit the inner loops strictly inside , on which we recursively call LoopGen.
Self-recursion The previous remark on inlining gives a way to handle some limited form of self-recursion. Indeed, we can inline the function itself at the position of the recursive call. More concretely, given the type of tree in Example 1 and a clause of the form Node(a,i,b) -> Node(f a,i,f b), it suffices to prefix all the moves by (. 0/ |. 2/). This prefix spans all the subterms on which  is called recursively. This requires adding alternatives inside paths. We believe our techniques to compute domains and dependencies still hold up with this extension. While this is not a fully 2 https://github. com/Drup/adtr/ general recursion scheme, it applies to functions such as map or transformations such as constant folding.
These relationships reflect fundamental features in human land use, regardless of the physical or cultural environment, and are thus useful for constructing generalized predictions of past human dispersal at broad spatial scales to test against archaeological data. We constructed a fully stochastic, cellular-automaton model at a resolution of 0. 5°× 0. 5°to quantify how the patterns of peopling of Sahul could have occurred based on measured ecological mechanisms. Stochastic, complex-system models like the one we present here are useful not only for forecasting phenomena like global and regional climate patterns  31  , biodiversity extinctions  32  and land use  33  , but also for hindcasting past phenomena based on hypothesized, but hitherto untested mechanisms  34, 35. Our model and analyses incorporate data-based human-population dynamics to project cell-specific population densities relative to hindcasted  35  , climate-dependent  36  predictions of maximum carrying capacity derived from established relationships between hunter-gatherer densities and environmental conditions  37  , databased rules of migration between neighbouring cells, measured long-distance dispersal  [38] [39] [40] [41]  contingent on water availability  15  and landscape ruggedness  42  (a proxy for accessibility), as well as spatially clustered catastrophic mortality events operating at a generational scale based on data from across vertebrate taxa  43.
Our model provides a baseline approximation for the relative timing and spread of human dispersal in Sahul on the basis of ecological parameters alone. The reliance here on ecological principles explicitly does not mean that human dispersal in Sahul did not involve deliberate social, cultural and/or economic decisions. Rather, the movement parameters we used were derived from a large global dataset of contemporary and recent hunter-gatherer populations, whose behaviours and decisionmaking processes were embedded with cultural knowledge and practices. The stochastic nature of the model allows the effect of cultural adaptation on human movement, as captured in the ethnographic dataset, to be expressed probabilistically as part of the modelled human-environment relationship. Here we show that full peopling of the entire continent of Sahul could have occurred within as few as 150-200 human generations (<5000 years), with up to 6.
The size of the long-distance-dispersing population followed the same rules as for neighbouring-cell emigration. Distance-to-water limitation. While territory size, and hence, maximum dispersal distances increase with increasing aridity according to the relationships described above, there is evidence that human dispersal is ultimately limited by water availability  15. This is likely to be even more relevant in Australia, the driest inhabited continent on Earth-indeed, estimated routes of gene flow among Aboriginal Australians suggest that the arid interior acted as a barrier to migration  11. We therefore invoked an additional limitation on dispersal by calculating a probability of realizing a long-distance dispersal event (P l ) according to the following equation previously designed to limit modelled species migrations 81 : P l ¼ 1 À D l D H 2 O ! Ω ð5Þ where D l = the realized maximum dispersal distance generated from the algorithm described above, D H 2 O = the distance to water in units of map cells derived from the Australian Water Observations from Space dataset  15  , and Ω = the hydrological resistance parameter set arbitrarily to a value of 3 to invoke landscape-scale resistance to movement only in the driest areas of Sahul per generational time step.
Ruggedness. We hypothesized that high landscape ruggedness (elevational gradient) might at least partially impede the progress of human expansion across the landscape  42  , so we tested this using data available in an ethnographic and environmental dataset compiled by Binford  42. Available in the binford library 82 in R, the dataset includes >200 variables measuring aspects of hunter-gatherer subsistence, mobility and social organization for 339 ethnographically documented groups. Given the evidence that mobility is a function of productivity  36, 80  , we constructed a simple linear model of annual movement varying with annual rainfall and the difference between maximum and minimum elevation within a 25-(40. 2 km) mile radius of the group's centroid (equivalent to an elevational gradient; i.
Acknowledgements This study was supported by the  Australian Research Council  through a  Centre of Excellence Grant  ( CE170100015 ) to R. G. R. , S. U. , M. I. B. , Z. J. , C. J. A. B. , S. O'C. , S. G. H. and L. S. W. , fellowships to S. U. ( FT120100656 ),  M. I. B. ( FL140100044 ),  R. G. R. ( FL130100116 ), Z. J. ( FT150100138 ), C. C. ( FT160100242 ), L. S. W. ( FT180100407 ) and B. L. ( FT170100448 ) and an  Australian   Government Research Training Program Award  to K. N. We acknowledge the Indigenous Traditional Owners of the land on which Flinders University is built-the  Kaurna people of the Adelaide Plains.
ARCHIPELAGO is an archaeological and historical database of land and sea food resources utilised in the Japanese Islands. Here we present a dataset of human bone and hair carbon and nitrogen stable isotopes measurements from Japanese archaeological sites covering the time span from the Upper Palaeolithic to the midnineteenth century. Reflecting the results of over 30 years of research, the dataset contains 1476 entries and covers the entire Japanese archipelago, although the data are more highly concentrated in coastal regions.
(1) OVERVIEW CONTEXT ARCHIPELAGO is an integrated archaeological and historical database of land and sea food resources utilised by humans in the Japanese Islands. Here, we present the first dataset from this initiative: human bone and hair carbon and nitrogen stable isotope measurements from archaeological sites in the Japanese archipelago covering the temporal range from the Upper Palaeolithic until the end of the early modern Tokugawa period (ca. 19 ,000 BC to AD 1868). The use of stable isotope analysis of archaeological human remains to reconstruct past diets began in the late 1970s  [1, 2]. The technique explores how certain food groups exhibit differences in their stable isotope compositions for certain chemical elements, most often carbon or nitrogen. The isotopic compositions of human tissues reflect those of consumed foods and stable carbon and nitrogen analysis of hair keratin and of collagen extracted from bone or teeth are particularly informative of protein sources, whereas stable carbon isotope measurements of human bone carbonate or tooth enamel reflect the mix of all food macronutrients  [3, 4].
STEPS Published stable carbon and nitrogen stable isotope data (δ 13 C and δ 15 N) were collected for premodern Japan up to the Meiji Restoration (1868). The majority of samples were derived from excavated human skeletons but historic hair from the early modern Tokugawa period (1603-1868) was also included. Where radiocarbon dates were not available, samples were cross-dated on the basis of pottery and other artefacts. Table  1  shows current widely-accepted dates for archaeological and historical periods in Japan, with the prehistoric chronology taken from Barnes  [22]. Geographic coordinates follow those reported by the organisation responsible for the excavation as published in the site report. These were reported using the Japanese Geodetic Datum (JGD2000) or World Geodetic System 84 (WGS 84).
(3) DATASET DESCRIPTION The dataset consists of a single table (available as "Japan human SI data v2. csv" and as "Japan human SI data v2. xlsx") deposited at the data platform of the Pandora initiative (https://pandoradata. earth/) within the ARCHIPELAGO community (https://pandoradata. earth/ organization/archipelago). The ARCHIPELAGO isotopic database is also a member of the IsoMemo initiative which brings together a network of isotopic databases (https:// isomemo. com) which includes a Webapp for querying and modelling of isotopic data (https://isomemoapp. com/). The data table consists of fields organized into thematic groups. Each data entry is identified by a unique sequential key (Entry_ID). The data submitter may include additional comments not covered by the existing fields (Comments), and identify the data submitter by name (ID_submitter).
Our dataset currently contains only human data but in the future we plan to expand it to include other taxa. Osteological information includes sex identification (Sex), a text description of age (Age_category_individual), numeric ranges in years for minimum (Min_age_individual) and maximum (Max_ age_individual) biological age of the individual at death, and the type of bone or hair material sampled from the individual (Sample_type). Biological age categories for skeletal individuals followed the published reports. These reports used standard bioarchaeological categories based on dental and skeletal age  [24] : infant: birth -3 years; child: 3-12 years; adolescent: 12-20 years; young adult: 20-35 years; middle adult: 35-50 years; old adult: 50 + years. In some cases, particularly with older publications, the ages of these categories may differ slightly.
The age >55 is sometimes used for 'older adults' in the Japanese literature. Cases where the same values are reported for minimum and maximum individual ages represent average estimated age. Some of the studies used here report very precise age estimates for sub-adults [e. g. ,  25 ]. In such cases our dataset follows these estimates, which are derived from different methods described in the studies concerned. The chronological range of the sample is given by a minimum age (Min_chronology) and maximum age (Max_ chronology) in years BC and AD with years BC expressed by negative numbers. Age assignment followed a hierarchical approach. Whenever available we employed direct dates from samples (e. g. radiocarbon dates, in which the calibrated 95% range is reported) or from coeval samples from the same archaeological context.
If necessary, corrections for marine radiocarbon reservoir effects were applied on direct radiocarbon measurements of human bones (see below). A dataset field (Dietary_ model_selection) identifies the type of Bayesian model employed to estimate the dietary contributions from marine carbon. If no secure dating was available from the sample context, we employed the site's chronology given usually in the archaeological report. If this was also not available, we employed the full cultural range to which the sample was assigned. A field was used to identify the type of employed dating method (Dating_method). Also included were fields for uncalibrated direct radiocarbon dates on sample (14C), its uncertainty (14_unc). Period tags are also used to provide traditional chronological information (Period_tags).
(4) BAYESIAN MODELLING OF DIRECT HUMAN RADIOCARBON MEASUREMENTS Our dataset contains 292 human samples for which chronological information is based on direct bone radiocarbon measurements. These measurements may be influenced by the consumption of aquatic foods, in particular marine foods from the seas surrounding Japan, which typically result in radiocarbon ages apparently older than the actual chronology of the analysed individual. This effect is known as a dietary marine radiocarbon reservoir effect (dietary MRE) and its correction requires an estimate of the contribution from marine carbon to human bone collagen plus an estimate of the MRE of consumed marine foods given that these can vary in space and time. Below we describe the use of Bayesian modelling to perform such a correction for the direct human radiocarbon measurements within our dataset.
Dietary estimates of marine carbon contributions to human bone collagen were obtained following similar procedures to those described in  [26, 27]. Briefly, we used the Bayesian software ReSources available via the IsoMemo Webapp (https://isomemoapp. com/) to generate the dietary estimates. This software is an upgraded version of the Bayesian software FRUITS allowing for the implementation of different Bayesian mixing model variants  [28]. We considered three models: model 1) a model that relies only on δ 13 C measurements and included four food groups (C3 terrestrial plants, terrestrial mammals, marine fish and marine shellfish); model 2) a model with four food groups relying on both δ 13 C and δ 15 N measurements (C3 terrestrial plants, terrestrial mammals, marine fish and marine shellfish); model 3) a model with five food groups relying on both δ 13 C and δ 15 N measurements (C3 terrestrial plants, C4 terrestrial plants, terrestrial mammals, marine fish and marine shellfish).
Models that do not include C4 plants (e. g. , millets) typically allow for dietary estimates of higher precision given that these have similar δ 13 C values to those of marine foods. Prior to the arrival of broomcorn and foxtail millet after 1000 BC, barnyard millet (Echinochloa utilis) was also likely cultivated by some Jōmon groups  [29, 30]. According to the radiocarbon database of the National Museum of Japanese History (https://www. rekihaku. ac. jp/ up-cgi/login. pl?p=param/esrd/db_param), the earliest directly dated Echinochloa remains from Japan are two seeds from Middle Jōmon Tomi-no-sawa (Aomori), here recalibrated to 95% credible intervals of 3006-2703 BC and 2879-2851 BC. However, most finds with direct dates are from the historic era. The majority of archaeological finds of barnyard millet also have a limited distribution in southwest Hokkaido/northeast Honshu.
400 years and so our reference cut-off of 1200 BC would correspond to c. 50% marine carbon dietary contributions (this is an approximate estimate given potential differences in local marine radiocarbon reservoir effects and fluctuations in terrestrial and marine calibration curves). Such high levels of marine consumption are not expected following the introduction of farming. For 30 individuals only δ 13 C values were available. Fortunately for these cases the calibrated radiocarbon ranges prior to dietary corrections clearly separated individuals dating older or younger than 1200 BC. For individuals dating younger we applied no model as higher stable carbon isotopic ratios likely reflect millet over marine consumption while for the remainder of individuals we employed model 1. Within the dataset, individuals with direct bone radiocarbon dates are tagged as 'none', 'model 1', 'model 2', and 'model 3' under the field 'Dietary_model_selection'.
5 ± 0. 5‰ towards dietary protein  [26]. In the case of human bone collagen δ 13 C we considered a routed model in which dietary protein contributed with 74 ± 4% of the collagen signal and the remaining 26% originated from dietary carbohydrates/lipids  [4]. The employed δ 13 C offset between diet and human bone collagen was 4. 8 ± 0. 5‰. We also employed a Bayesian prior that limited the contribution of dietary protein to between 10 and 35% of total calories in accordance with physiological studies  [35]. The ReSources software generates different dietary estimates but for chronological purposes estimates of marine carbon contribution towards bone collagen are the relevant ones. These were expressed as a mean and standard deviation and represent the sum of estimates obtained separately for marine fish and shellfish.
Full specification of the models is given in R workspace files ("Model 1. Rdata", "Model 2. RData", "Model 3. RData") made available at the ARCHIPELAGO depository. Estimates were generated for all individuals irrespective of their C/N atomic ratio values. However, as mentioned previously, bone samples for which C/N atomic ratio values are outside acceptable ranges should not be employed for dietary studies and are included in our dataset only for preservation studies. In addition to dietary estimates, a marine reservoir effect human dietary correction also requires an estimate of the magnitude of the MRE of consumed marine species. For these we lack approximate contemporaneous values for each individual. Thus, we relied on radiocarbon measurements on modern pre-bomb marine samples listed in the Marine Reservoir Correction Database  [36].
Using this data, we produced an estimate of the spatial variations in marine ΔR (representing the local MRE offset from the marine calibration curve) along the coast of Japan (Figure  3 ) using the Bayesian model AverageR available via the IsoMemo Webapp  [37, 38]. Average and standard deviation values for ΔR of marine foods for each individual within the dataset were obtained by considering an area within a 100 km radius around assigned geographical coordinates. Radiocarbon calibration was done using the Bayesian chronological software OxCal v. 4. 4  [39]. We employed a mixed curved model consisting of the terrestrial IntCal20 calibration curve for the northern hemisphere  [40]  and the Marine20 marine calibration curve  [41]  where the contribution from Marine20 is the isotope-based normally distributed dietary estimate for each individual.
DATASET CREATORS The primary researcher responsible for the metadata structure and modelling was Ricardo Fernandes and for data collation was Mark Hudson. The collected dataset combines isotopic data, informative of diet, with chronological, osteological, cultural, and other types of archaeological and historical information. This provides the basis for future research on the association between ancient diets and socioeconomic status, cultural and religious choices, and local paleo-environmental and palaeo-climatic conditions, among others. We also aim at investigating spatial and diachronic trends in dietary patterns across Japan using the modelling tools available via IsoMemo (https:// isomemoapp. com/).
ACKNOWLEDGEMENTS The research leading to these results has received funding from the  European Research Council  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement no. 646612 ) granted to  Martine Robbeets. Pandora & IsoMemo  initiatives are supported by  Max Planck Institute for the Science of Human History , PS&H research group,  University of Warsaw, Masaryk University , and the  Eurasia3angle research group. We thank the two reviewers for their comments on an earlier version of this paper.
The Comprehensive Database of Archaeological Site Reports in Japan run by the Nara National Research Institute for Cultural Properties (https:// sitereports. nabunken. go. jp/en) was used to search site reports. PERIOD GENERIC PERIOD DATE RANGE BC/AD Palaeolithic Palaeolithic Up to 14,520 BC Incipient Jōmon Jōmon/Neolithic 14,520-10,550 BC Initial Jōmon Jōmon/Neolithic 10,550-5050 BC Early Jōmon Jōmon/Neolithic 5050-3520 BC Middle Jōmon Jōmon/Neolithic 3520-2470 BC Late Jōmon Jōmon/Neolithic 2470-1250 BC Final Jōmon (southwest Japan) Jōmon/Neolithic 1250-970 BC Final Jōmon (northeast Japan) Jōmon/Neolithic 1250-400 BC Initial Yayoi Yayoi-Kofun 1000-800 BC Early Yayoi Yayoi-Kofun 800-450 BC Middle Yayoi Yayoi-Kofun 450 BC-AD 50 Late Yayoi Yayoi-Kofun AD 50-250 Early Kofun Yayoi-Kofun AD 250-400 Middle Kofun Yayoi-Kofun AD 400-500 Late Kofun Yayoi-Kofun AD 500-710 Epi-Jōmon (Hokkaido) Yayoi-Kofun 340 BC-AD 700 Nara Nara-Heian AD 710-794 Heian Nara-Heian AD 795-1185 Okhotsk & Satsumon Medieval Hokkaido AD 500-1200 Kamakura (early medieval) Medieval Japan AD 1185-1333 Muromachi & Azuchi-Momoyama (late medieval) Medieval Japan AD 1333-1603 Tokugawa (early modern) Early modern AD 1603-1868 Table 1 Sample periodisation employed in the dataset.
Co-infections with multiple pathogens are common in the wild and may act as a strong selective pressure on both host and parasite evolution. Yet, contrary to single infection, the factors that shape co-infection risk are largely under-investigated. Here, we explored the extent to which bird ecology and phylogeny impact single and co-infection probabilities by haemosporidian parasites using large datasets from museum collections and a Bayesian phylogenetic modelling framework. While both phylogeny and species attributes (e. g. size of the geographic range, lifehistory strategy, migration) were relevant predictors of co-infection risk, these factors were less pertinent in predicting the probability of being single infected. Our study suggests that coinfection risk is under a stronger deterministic control than single-infection risk.
Avian samples and parasites detection Our data set includes 1361 samples of 151 species encompassing 44 families and 18 orders (Appendix 1, Table  S1 ). Sampling was conducted on salvaged birds that were obtained between 1990 and 2019. and consisted of tissues (muscle and liver) stored in 85% ethanol at 4°C at the Cantonal Museum of Zoology in Lausanne (855 specimens) and in 90% ethanol at -20°C at the Natural History Museum of Geneva (506 specimens). For each individual, parasites (i. e. Haemoproteus,  Leucocytozoon and Plasmodium)  were detected from tissue samples using molecular methods. Specifically, a nested PCR  (Hellgren et al. 2004 ) was performed in triplicates on all samples after DNA was extracted from tissues using a DNeasy Blood & Tissue Kit (Qiagen, Switzerland) following the manufacturer's instructions.