dataesr/scientific-paragraphs-categorization huggingface dataset:
Software architecture Higra is composed of a core, header-only, C++ 14 module, where performance critical data structures and algorithms are implemented, and of a Python module exposing the C++ module API and proposing higher level functions. In Higra, graphs are analysed through their hierarchical representations. The hierarchical representation of a graph corresponds to a tree where each node is a cluster, also called a region, of the input graph. The two main data structures of the library are thus the graph class, implemented as an adjacency list, and the tree class, implemented as a parent array  [11]. These classes are lowly coupled to their associated data, vertex and edge weights for graphs, and node weights for trees. Those data are represented by multi-dimensional arrays using the xtensor library [12] in C++ and Numpy  [13]  arrays in Python, hence enabling their easy and efficient manipulation with standard array programming.
This separation is achieved by imposing that all the elements of a graph (vertices and edges) and all the elements of a tree (nodes) are identified by integers indices ranging from 0 to the number of elements minus one. Moreover, in order to facilitate the exchange of data between a graph and its hierarchical representation as a tree, we also impose that the indices of the tree leaves are exactly the indices of the vertices of the graph. In other words, an array representing vertex features on the graph can be used as an array representing leaf node features on the tree. All dependencies of the core C++ module are header-only C++ library, thus ensuring that the library can be easily compiled and extended on a large variety of systems. C++ functions and data structures are mapped to Python types with Pybind11 [14].
Software functionalities Higra contains a large amount of classical and recent algorithms for the construction, the manipulation, and the analysis of hierarchical graph representations: • efficient methods and data structures to handle the dual representations of hierarchical clustering: trees  [15]  (dendrograms) and saliency maps  [16]  (ultrametric distances); • hierarchical clusterings: quasi-flat zone hierarchy  [17] , hierarchical watersheds  [18, 19] , agglomerative clustering  [20]  (single-linkage  [11, 21] , average-linkage, complete-linkage, exponential-linkage  [22] , Ward, or user-provided linkage rule), constrained connectivity hierarchy  [23] ; • component trees: min and max trees  [24, 25] ; • manipulate and explore hierarchies: simplification  [26, 27] , accumulators, cluster extraction, various attributes  [28]  (size, volume, dynamics, perimeter, compactness, moments, etc.
Impact Up to our knowledge, Higra is the first library that gathers so many algorithms for hierarchical graph analysis in one place. As such, providing reference implementations of classical and state-of-the-art algorithms will already provide great benefit by favouring the spread of those methods among the community, by easing reproducible research, and by helping the creation of relevant comparisons between methods. As Higra works on generic graphs and does not assume a particular application, it can be used in a large variety of fields. While its usage requires some knowledge on hierarchical representations, it provides an easy-to-use Python interface which is extensively documented. This can further help to spread the use of the proposed approaches in the scientific community as well as serving as a pedagogical tool for teachers.
While some algorithms still have to be written in C++ to ensure high performances, we have found that the set of basic operators provided in the library used in conjunction with the vectorization of many operations on hierarchies indeed enables to directly write many algorithms acting on hierarchies efficiently in Python in a Numpy style. This enables to considerably simplify and speed up the development of new methods compared to classical C/C++ programming. Higra is a new library, and as such, still has a limited base of contributors and users. Some of its core functions have been used for a long time in our research group and led to dozens of publications  [11, 16, 19, 32, 34, [44] [45] [46] [47] [48]  (among others) and the usage of the library is spreading in new publications  [26, 49].
Acknowledgements The authors are grateful to  CNPq  ( Universal 421521/2016-3  and  PQ 307062/2016-3 ),  FAPEMIG  ( PPM-00006-16  and  PPM-00006-18 ) and  PUC Minas  for the financial support to this work. This study was financed in part by the  Coordenação de Aperfeiçoamento de Pessoal de Nível Superior -Brasil  ( CAPES) -Finance Code 001  and by  CAPES/COFECUB 88887. 191730/2018-00. We also acknowledge financial support from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No  721463  to the  SUNDIAL ITN  network.
Intermittent-flow respirometry is widely used to measure oxygen uptake rates and subsequently estimate aerobic metabolic rates of aquatic animals. However, the lack of a standard quality-control software to detect technical problems represents a potential impediment to comparisons across studies in the field of evolutionary and conservation physiology. Here, we introduce 'FishResp', a versatile R package and its graphical implementation for quality-control and filtering of raw respirometry data. Our goal is to provide a straightforward, cross-platform and free software to help improve the quality and comparability of metabolic rate estimates for reducing methodological fragmentation in the field of aquatic respirometry. FishResp accepts data from various respirometry systems, allows users to detect potential mechanical problems which can occur during oxygen uptake measurements (e.
g. chamber leaking, poor water circulation), and offers six options to correct raw data for microbial oxygen consumption. The software performs filtering of raw data based on user criteria, and produces accurate and unbiased estimates of absolute and mass-specific metabolic rates. Using data from threespined sticklebacks (Gasterosteus aculeatus) and Trinidadian guppies (Poecilia reticulata), we demonstrate the virtues of FishResp, highlighting the importance of detecting mechanical problems and correcting measurements for background respiration.
, 2015; McDonnell and Chapman, 2015; Hancock and Place, 2016; Kunz et al. , 2016; Norin et al. , 2016; Jayasundara et al. , 2017; Zhang et al. , 2017). In addition, intermittent-flow respirometry has been recently used in studies devoted to both applied and fundamental problems in aquaculture and fisheries  (Hessenauer et al. , 2015; Siikavuopio and James, 2015; Gerile and Pirhonen, 2017; Svendsen et al. , 2018). Despite the growing use of respirometry, quality-control of raw data and extraction of key parameters from it, remains the responsibility of individual researchers  (Rodgers et al. , 2016). The lack of standard quality-control software with the ability to detect and correct for technical problems in the raw data may compromise the comparability of results from different studies. Indeed, the literature contains many contradictory reports of intraspecific metabolic differences that are difficult to ascribe to only biological causes.
In addition, background respiration may represent an increasingly large proportion of the total oxygen consumption under hypoxic conditions. Here, we introduce 'FishResp', software for running quality control tests, filtering raw data and calculating metabolic estimates of high accuracy. We demonstrate the utility of FishResp for detecting mechanical problems and correcting for background microbial oxygen consumption using raw data obtained from the three-spined stickleback (Gasterosteus aculeatus) and the Trinidadian guppy (Poecilia reticulata). These examples demonstrate how FishResp significantly improves the quality of final estimates, setting what we hope will become standard practice for processing raw respirometry data.
Software description FishResp is an R package (R Development Core  Team, 2018)  for analysing raw data from intermittent-flow respirometry systems. Its current version 1. 0. 2 consists of 15 functions (listed in Table  1 ) and utilizes six dependencies (existing R packages) which will be automatically installed once FishResp is executed (listed in Table  2 ). The order of application of each function in a typical analysis is described in Fig. 1 , encompassing all steps from data input to parameter estimation. Alternatively, a graphical user interface (GUI) of FishResp is also available, written in JavaFX. To integrate Java with R, the 'rJava' package  (Urbanek, 2018)  and its extension 'JRI' are used. The graphical interface consists of five tabs with functional blocks on the left side (functions with parameters), and information, graphical or extensions modules on the right side of the application (Fig.
2 ). The 'Extensions Module' has been created to integrate FishResp with other respirometry tools written in the R language, such as modified functions for unit conversion from the R packages 'respirometry' and 'rMR'  (Birk, 2018; Moulton, 2018). Different colours of tabs and buttons are used as indicators of progress: unavailable (grey); plotting optional (blue); action required (yellow); successful execution (green). The log of complete actions can be saved using an icon in the upper-right corner of the graphical interface.
2 imported into the R package/GUI application directly from 'AutoResp' (Loligo Systems, Denmark) and 'Q-box Aqua' (Qubit Systems, Canada); and via 'AquaResp' (University of Copenhagen, Denmark) with 'OxyView' (PreSens GmbH, Germany) and 'Pyro Oxygen Logger' (Pyro Science GmbH, Germany) output. The output from other logger software and DIY setups can be manually adjusted to 'FishResp' import format (see 'Details' in the documentation of the R function 'import. test' or 'import. meas'). The software accepts several units of O 2 concentration, while partial pressure units (e. g. % sat, mmHg or kPa) can be converted to O 2 concentration using the R functions 'convert. respirometry' and 'convert. rMR' or in the 'Extensions Module' of the GUI application (for details see the documentation of those functions).
Experimental design for correction of background respiration The number of traits calculated per one session is not limited in the R package, while GUI implementation of FishResp can be used only for measuring one or two metabolic traits per session, as shown in Fig. 3. In order to subtract background respiration from total oxygen consumption, additional tests must be conducted without an animal in the chambers Convert raw data from OxyView (PreSens) or Pyro Oxygen Logger (PyroScience) along with a summary file from AquaResp (free software) to the FishResp format The functions are listed in order of a typical FishResp analysis (see also Fig. 1 ), excluding optional functions convert. respirometry, convert. rMR, presens. aquaresp, pyroscience. aquaresp. A full experiment processed with the GUI application can consist of four different steps (Fig.
Fresh water was constantly mixed by a pump during the measurement phase (T = 24. 8°C; mean cO 2 = 7. 1, SD = 0. 5 mg/L). Background respiration in the chambers was measured before and after the experiment (Fig. 3B ). The calibration of the oxygen sensors for case studies was performed following the 2-point calibration procedure described in the AutoResp user's manual (Loligo Systems, Denmark). Raw data for both Case Study 1 and Case Study 2 can be found in the package folders 'FishResp/extdata/stickleback' and 'FishResp/extdata/guppy,' respectively. Experiments were carried out in accordance with the recommendations and approval of the National Animal Experiment Board of Finland (permit no: ESAVI/6097/ 04. 10. 07/2013) and the Stockholm Ethical Board (permit no: N173/13, 223/15 and N8/17).
MS of Sticklebacks Import and correction of raw data Following the general outline of a typical 'FishResp' analysis (Fig. 1 ), the command library(FishResp) is used to load the package. The function input. info() must be applied first to specify dissolved oxygen measurement units, and to provide information about individual ID, fish mass (g), and chamber volumes (mL) used in subsequent analyses (see Table  1 ): info <input. info(DO. unit = "mg/L", ID = c("Stickleback_1", "Stickleback_2", "Stickleback_3", "Stickleback_4"), Mass = c(1. 86, 1. 92, 2. 23, 1. 80), Volume = c(250, 250, 250, 250)) To load files containing raw data of background respiration measurements into R ('pre-test. txt' and 'post-test. txt'), the function import. test() should be used-note that if data files are not located in the same directory as the R session, file names should be specified with their full paths (see code in File S1).
The path to the file, the type of oxygen logger software, and the number of chambers used should also be included as arguments. Graphs of oxygen and temperature over time are plotted automatically to check temperature fluctuations and water supply to the sensor during background respiration tests: pre <import. test(pre. path, info. data=info, logger="AutoResp", n. chamber=4) post <import. test(post. path, info. data=info, logger="AutoResp", n. chamber=4) The next step is to import raw data of SMR and AMR measurements into R using the import. meas() function. If the date format is not 'DMY', specify it in this function. Two additional parameters (start. measure and stop. measure, respectively) corresponding to the time interval of measurements not exceeding 24 h can help remove non-target data (e.
MR(). SMR <calculate. MR(SMR. slope, density=1000) Results can be exported as. txt or. csv files. Optionally, when two traits are measured, they can be merged into one dataset containing absolute, mass-specific and factorial metabolic scopes. results <export. MR  (SMR, AMR, "results. csv", simplify=TRUE, MS=TRUE)  Note that the rate of background respiration for both SMR and AMR is not more than 13. 8% and 28. 7% respectively. However, the situation can change dramatically when the experiment is conducted at high water temperature and/ or in small chambers, as demonstrated by Case Study 2. Case Study 2: The effect of background respiration on SMR of Guppies.
Running the analysis using GUI application The GUI version is created for users who prefer to interact with graphical elements in a user-friendly interface instead of working with a command line. Here, we demonstrate the use of the application for estimating background respiration, and calculating SMR (see File S2 to run this example in R). After launching the program, a user should set parameters in the 'Global settings' module: we choose 'preand post-tests' as background respiration tests, 'AutoResp' for the type of logger software, and eight chambers in the respirometry system. As we measured only one trait (SMR), the field for 'Trait 2' should be empty. Click the 'Save global settings' button, and fill in the table with information about animals and chambers (Fig. 2 ). Next, move to the 'Background respiration' tab and import data for pre-and post-tests.
10 lead to underestimation and/or overestimation of metabolic rate. FishResp software, used together with a proper experimental design  (Clark et al. , 2013; Svendsen et al. , 2016b) , represents a simple and efficient approach to account for the effects of chamber issues and background respiration in the processing of raw respirometry data. To make FishResp a universally available tool, we have developed both the R package (available on CRAN), and its cross-platform and user-friendly graphical implementation under open source licenses. Both can import raw data from the majority of respirometry systems available on the market. FishResp can be adjusted for different experimental designs: the software allows users to choose among various options for detection of mechanical problems, correction of background respiration, and calculation of metabolic rate.
Data availability The R package and data are available on the CRAN repository (http://cran. r-project. org/package=FishResp). R scripts for running both case studies are provided in Supplementary files S1 & S2. Application of functions which have not been reviewed in the case studies are shown in Supplementary file S3. The official website (https://fishresp. org) will host all information related to FishResp, including user manuals, tutorial videos, FAQ, a forum and a blog with the latest updates of the software.
Region For the whole Neotropics, freeware online database:  Bush and Weng (2007)  For Amazonian taxa: Counting is routinely done with a light microscope at 400x although use of an oil immersion objective allowing 1000x magnification is required in some cases  (Birks and Birks, 1980). The number of pollen grains and spores counted varies depending on the research objectives although it should be enough high to reach constant percentages of the different taxa and at least exceed a minimum count of 100 to calculate the relative proportions (expressed as percentages of the pollen sum). For terrestrial sediments, 300 to 500 grains are usually counted  (Birks and Birks, 1980). For marine sediments, counting usually aims to reach a total of 300 pollen and spore grains with at least 100 pollen grains excluding Pinus, a well-known over-represented taxon  (Desprat, 2005; Turon, 1984).
, 1994; Runge, 1999). Phytoliths of Musa are volcaniform (o)  (Ball et al. , 2016)  when the ones from Cucurbita are spheroidal or hemispheroidal with deeply scalloped surfaces of contiguous concavities  (Piperno et al. , 2000). Other specific phytoliths can be attributed to rice, Maize or Marantaceae (see the exhaustive discussion in Piperno (  2006 )). 5. Non-diagnostic morphotypes (p) such as globular smooth, elongated or tabular and blocky types are sometimes attributed to specific vegetation types, such as closed environments. However, the diversity of shapes behind the generic terms makes it difficult to be exhaustive for this category (see  Garnier et al. , 2012; Novello et al. , 2012; Runge, 1999). Comprehensive databases and atlases for phytolith identification do not exist yet. The web and scientific papers provide some atlases but the data are diverse, dispersed and not easily comparable.
From these studies arose several regional modern pollen databases for Europe  (Davis et al. , 2013; Fyfe et al. , 2009) , North America  (Whitmore et al. , 2005) , East Asia  (Zheng et al. , 2014) , Africa  (Gajewski et al. , 2002)  and South America  (Flantua et al. , 2015). Surface sample studies have shown there is no linear relationship between pollen proportions and plants abundances. Pollen proportions from a sedimentary archive give qualitative information on changes in vegetation composition through time and over a spatial area. Many studies demonstrated that pollen assemblages clearly discriminate between vegetation formations or forest-types and that pollen proportions of the major taxa reflect their relative importance in the vegetation  (Prentice, 1988). Individual calibration studies prior to the analysis of a sedimentary archive are recommended to provide the characterization of the relationship between the pollen rain and local and regional vegetation essential to interpret the fossil pollen records in terms of vegetation changes.
e. explicit assumptions) on pollen productivity estimates (PPEs), lake size and wind speed. PPEs are difficult to estimate and time and resource consuming considering that taxon pollen productivity presents a high regional and interannual variability. Since all these parameters may vary through time, treating them as known processes increases the uncertainties of REVEALS land-cover reconstructions. However, REVEALS remains the only method that addresses these uncertainties directly, and at the taxon level. In addition, there are also subtle differences in the nature of the land-cover reconstruction between methods. For instance, REVEALS reconstructs the proportion of the land cover occupied by taxa in the landscape, irrespective of their physical size. In contrast, MAT methods often have a specific physical definition associated with the remote sensing datasets that are used for calibration, such as a minimum height (e.
g. greater than 5 m) for forest taxa. This has implications in, for instance, shrub dominated landscapes such as forest-tundra landscapes where REVEALS may indicate more "forest" than MAT due to the low physical stature of the taxa  (Zanon et al. , 2018 ). An alternative and less resource intense approach has been developed by  Williams (2003) , based on the popular modern analogue technique (MAT) applied in pollen-climate reconstructions, whereby analogues of fossil pollen samples are found in a modern pollen database. In the land-cover reconstruction method, the fossil sample is assigned to the remotesensing derived forest cover of the closest matching modern pollen sample site. This method is particularly useful to reconstruct past forest cover at continental scales. It was used to reconstruct Holocene forest cover in North America  (Williams, 2003) , Europe  (Zanon et al.
Since the first pollen-climate transfer function over 70 years ago, there has been a large number of different methods developed, largely motivated by the problems that we have already outlined. These methods can be grouped into four main groups: 1) The first and generally the most popular group of methods is based on matching an assemblage of taxa present in a fossil pollen sample with unknown climate, with the same assemblage in a modern pollen sample whose climate is known. This includes the classic modern analogue technique (MAT), but also variants such as response surfaces  (Brewer et al. , 2007). Advantages include simplicity and an ability to incorporate non-linear responses to climate, while disadvantages include the need for a large calibration dataset of modern pollen samples and poor statistical treatment of uncertainties.
2) The second group of methods builds a regression model for each taxon based on the relationship between modern pollen samples and known modern climate, which is then used to deduct the past climate from the taxa in a fossil pollen sample assemblage. This includes the popular Weighted Averaging -Partial Least Squares (WA-PLS) method  (Birks et al. 2010). The advantage of this method includes better statistical treatment of uncertainties and elimination of problems such as spatial auto-correlation that are common to MAT. However disadvantages include heavy reliance on capturing the correct climate response within the calibration dataset and poor performance at the edges of the response envelope. 3) Both the previous two groups of methods require an extensive and representative modern pollen surface sample dataset for calibration of the transfer function, and also rely on the relative proportions of the taxa in the pollen assemblage.
The third group of methods instead uses modern vegetation distribution rather than modern pollen samples as the basis for calibrating the transfer function, and generally uses presence and absence of taxa rather than its proportional occurrence in the assemblage. This includes classic methods such as mutual climatic range, as well as the more recent probability density function approach  (Chevalier et al. , 2014). These methods work by establishing the climate envelope for each taxon based on its modern vegetation distribution, and then combining the envelopes of the taxa found in the fossil pollen assemblage to deduce the most likely climate where all the taxa are able to exist together. This group of methods does not require a calibration dataset of modern pollen samples. They are especially good in areas where these datasets are limited such as in Africa  (Chevalier et al.