dataesr/scientific-paragraphs-categorization huggingface dataset:
Submitting sequence and tree data during publication is now routine. However, making available all analytical methods such as software and code used to process and analyze data is less widely employed by the phylogenetic community. Facilities such as TreeBase, Dryad Digital Repository, and Github (https://github. com/) provide a platform for the curated storage of the data and bioinformatic pipelines underlying the scientific literature (see  McTavish et al. , 2015; 2017). Authors and journals should require all published research to include links to raw data, processed data, and all analytical methods used to produce the results presented. In general, we advocate for following best practices of data management and publication to ensure the quality and utility of phylogenomic data and their associated biological information  (Stoltzfus et al.
Given their key role in phylogenomic research, it is advisable that software development, documentation, and availability follow the best possible practices (e. g. ,  Leprevost et al. , 2014; Wilson et al. , 2014; Guang et al. , 2016; Darriba, Flouri & Stamatakis, 2018; DeBiasse & Ryan, 2018). Having both data and analytical tools adequately stored and accessible to the public not only will ensure high reproducibility of previous studies, but, more importantly, will facilitate continuing the construction of the ToL  (McTavish et al. , 2017). All contributions toward building the Tree of Life must be properly recognized Some current publishing practices in the scientific community may unintentionally represent hurdles toward the ultimate end of collecting and disseminating phylogenetic data on which to build a ToL.
A posteriori (after data generation) marker selection from whole-genome alignments can be useful to inform these aspects as well as minimize the effects of missing data and varying data quality. Until then, researchers should attempt a higher standardization of markers to facilitate combinatory analyses. To discern true phylogenomic heterogeneity from noise and error as well as to identify violations of the MSC, adequate filtering of large phylogenomic datasets should be conducted based on biological and statistical properties of markers (e. g. , analyses of gene-tree outliers and rogue taxa). Further research on filtering methods as well as on their impact on phylogenomic estimation is still required. Inclusion of additional character types into phylogenomic analyses. Research efforts focused on the adequate identification and utilization of rare genomic changes other than nucleotide substitutions, such as indels, transpositions, inversions, CNVs, and chromosomal rearrangements.
Phylogenetic inference models and methods Analyses of genome-scale data for large numbers of species within a coalescent framework. Continue the development of models and methods that allow simultaneous gene tree and species tree estimation within a Bayesian framework (e. g. , Ogilvie, Bouckaert & Drummond, 2017) for increasingly large and complex datasets. For the time being, two-step methods, particularly those based on biological models and permitting statistical tests of topologies in a likelihood framework (e. g. ,  Liu, Yu & Edwards, 2010) , are useful tools to incorporate coalescent information into species tree inference. Detection and incorporation of violations of the MSC into phylogenomic inferences. Extensions of the MSC should seek the inference of reticulate evolutionary histories (i.
It is time to strengthen those initiatives aimed at recognizing scientific production beyond citations of peer-reviewed literature (e. g. , ORCID; https://orcid. org) by giving also credit to the production and impact of basic biology datasets and collected specimens. Providing credit for depositing and generating data by tracking, for example, number of access and downloads or number of studies using genetic data associated to specimens, could represent a formal recognition of the importance of producing and sharing basic biological data could help bridge the gap between naturalists, taxonomists, empiricists, and mathematicians invested on the study of life history. It will be exciting to have objective estimates that allow tracking the direct and indirect impact of how these data and samples are being used.
ACKNOWLEDGEMENTS This paper is a product of the ' Origin of Biodiversity Workshop ' organized by  Chalmers University of Technology  and the  University of Gothenburg , under the auspices of the  Gothenburg Centre for Advanced Studies (GoCAS). We are particularly grateful to the GoCAS organizers and facilitators, in particular  Karin Hårding ,  Mattias Marklund ,  Bernt Wennberg ,  Sandra Johansson , and  Lotta Fernström. We thank  Johnathan Clark ,  Alison Cloutier ,  Phil Grayson ,  Kathrin Näpflin ,  Flavia Termignoni ,  Jonathan Schmitt ,  Simon Sin ,  João Tonini , and  Pengcheng Wang  for help compiling Table  S1. Thomas Couvreur ,  Tobias Andermann ,  Prosanta Chakrabarty ,  Alex Pyron ,  Claire Morgan ,  Chris Creevey , and one anonymous reviewer provided useful comments that improved the contents of this manuscript.
Salinity. The sound speed v can be calculated from pressure, temperature, and salinity  32. We use the empirical relations, assuming a constant salinity, as in similar acoustic experiments  33, 34. Temperature. For the time after 5th May 2017 until January 2018 the F-stations were still operable, but the temperature sensors of the F-stations do not allow estimating a high-resolution baseline since artefacts of their temperature sensor cannot be isolated from a baseline change. Both networks operated independently but measured some common baselines (Fig. 2 ). We use the temperature data from the G-transponders for the close-by F transponders (distances less than 100 m each) since they have a high-resolution temperature sensor. The temperature timeseries from the G-stations (sample interval of 90 min) were spline interpolated onto the measurement times of the close by F-stations (six measurements during the first 10 min of each hour).
1 ). The OBS network was installed in the shipping lane of the access to the Bosphorus in order to avoid station loss due to fishing activities. As a result, the elongated installation prevents very accurate estimates of the event depths using OBS only. Therefore, all events shown with red circles were located with land and OBS observations. Only events with RMS values smaller then 1 s and recorded on more than five stations are used. Events were located using the 1D velocity model from  50  and the seismological analysis software SEISAN  51. The OBS stations did not significantly increase the number of events detected close to the geodetic network. Few additional events were detected compared to Koeri's catalogue, but P-S times (always larger than 2 s) and P polarisation indicate that these events did occur outside the OBS network and hence the geodetic network.
Acknowledgements The acoustic seafloor monitoring in the  Sea of Marmara  is a joint project of the  Istanbul Technical University (Turkey ) together with the  University of Brest (France)  and  GEOMAR Helmholtz Centre for Ocean Research Kiel (Germany). We thank  C. Hammersley  and  T. Bennetts  from  Sonardyne  for their technical support with the geodetic instruments over the years. We thank the crews and Captains of R/V Pourquoi Pas?,  R/V Poseidon ,  R/V Yunus  and Oktay 8 for their support during installation and maintenance of the network. The  GeoSEA Array  is funded through grant  03F0658I  of the  BMBF (German Federal Ministry of Education and Research). Support was provided by the bilateral  ANR/TU ̈BITAK collaborative research  project  MAREGAMI  ( ANR-16-CE03-0010-02  and  Tubitak  Project  116Y371 ).
Data availability The geodetic data that support the findings of this study can be downloaded through the data repository PANGEA [G-stations, data https://doi. org/10. 1594/PANGAEA. 900275]  52 nd through SEANOE [F-stations, data https://doi. org/10. 17882/59750]  53. The phase picks from the onshore seismological data is from KOERI (downloaded 15 June 2018). OBS data can be requested from the corresponding author.
We performed all qPCR analyses using a CFX Connect Real-Time PCR Detection System from Bio-Rad. We then transformed Cq values into raw copy numbers and used the DNA isolation efficiency to normalize the raw R. irregularis copy numbers. Probe design, qPCR calibration, detection limits, and plasmid preparation are as described in  [53]. To convert copy numbers to biomass, we created a calibration curve (R 2 = 0. 70; N = 26) of AM hyphal biomass against their corresponding copy numbers determined by qPCR. This approach was re-validated by recent work testing the abundance and marker relationship when looking within a single AM fungal isolate  [68] , where it was demonstrated that mtDNA markers generate well-correlated abundance data and can be used successfully to quantify particular isolates.
52) and a 40x phase contrast objective (Nikon, S Plan Fluor ELWD, 40, Ph2, NA 0. 6). For each field of view, we recorded 200 frames at 10Hz and an exposure of 30ms using a PCO. edge sCMOS camera. We imaged the network of six replicate hypha at three time points (0, 30 and 60 minutes) under control conditions (no QD-apatite added) to obtain movies suitable for particle tracking. We processed each set of images using FIJI  [41]  to identify traceable cellular contents. This included image acquisition, pre-processing (e. g. , noise removal with Gaussian Filter, filtering with FFT), segmentation (e. g. , particle identification, threshold/binarization), particle tracking and data analysis. The processing steps were performed in all movies independently. We applied an FFT bandpass filter to remove structures larger than 40 px and smooth features smaller than 3 e6 Current Biology 29, 2043-2050.
DATA AND SOFTWARE AVAILABILITY All statistical analyses were performed in R 3. 4. 4. We detail our full analyses, including all diagnostics plots, in an R-Markdown report. Our scripts and analyses are publicly available through a GitHub repository (https://github. com/gijsbertwerner/ Mycorrhizal_inequality) and in a Dryad data repository (doi:https://doi. org/10. 5061/dryad. n343sh3).
ACKNOWLEDGMENTS We thank  J. Weedon ,  R. F. Denison ,  E. Hammer ,  M. Rillig ,  A. Bago , and  J. Jansa  for very helpful feedback, and  I. Sanders ,  J. Jansa , and  B. Hause  for biological material. Research was supported by  Netherlands Organisation for Scientific Research Vidi  grants  864. 10. 005  to  E. T. K. ,  864. 09. 015  to M. P. , and  680. 47. 515  to T. S. S. ; Middelgroot grant  834. 09. 003  to M. A. H. ;  European Research Council ERC  grant agreement  335542  to E. T. K. ; a  Royal Society Newton International Fellowship  to G. D. A. W. ; and the  Paul G. Allen Family Foundation  to T. S. S.
Drainage basins are essential to Geohydrology and Biodiversity. Defining those regions in a simple, robust and efficient way is a constant challenge in Earth Science. Here, we introduce a model to delineate multiple drainage basins through an extension of the Invasion Percolation-Based Algorithm (IPBA). In order to prove the potential of our approach, we apply it to real and artificial datasets. We observe that the perimeter and area distributions of basins and antibasins display long tails extending over several orders of magnitude and following approximately power-law behaviors. Moreover, the exponents of these power laws depend on spatial correlations and are invariant under the landscape orientation, not only for terrestrial, but lunar and martian landscapes. The terrestrial and martian results are statistically identical, which suggests that a hypothetical martian river would present similarity to the terrestrial rivers.
III. RESULTS We applied the IPBA model to real and artificial landscapes in order to study the statistical properties of the drainage basins on Earth, Moon, and Mars. For real landscapes, we use three different DEMs throughout this study: the General Bathymetric Chart of the Oceans (GEBCO)  [49] , the Lunar Orbiter Laser Altimeter (LOLA)  [50] , and the Mars Orbiter Laser Altimeter (MOLA)  [51]. Such datasets consist of the map of heights for the Earth, Moon and Mars, respectively. Moreover, we obtained the artificial landscapes through the fractional Brownian motion (fBm)  [55]. We chose this method because we are interested in generating landscapes with tunable spatial long-range correlations since it is known that such correlations change the statistical properties of watersheds  [24]. We show all landscapes in Figs.
2A-C  and Figs. 3A-D. We considered two scenarios for GEBCO, LOLA, and MOLA datasets: the original and the upside-down landscape orientations. We emphasize that we chose the GEBCO dataset to represent the terrestrial landscapes because it includes altimetric and bathymetric heights, which makes it more similar to LOLA and MOLA datasets. In other words, we used the GEBCO dataset in order to make a general and uniform comparison between Earth and two celestial bodies that do not have oceans (Moon and Mars). To perform a global analysis, the resolutions of the real datasets were decreased by a factor of r = 8, where r is the resolution factor. Thus, each tile of 8 × 8 sites was replaced by a single site with a value given by the mean of all 64 original sites. The sizes of the used lattices were 5400 × 2700 for GEBCO (original 43200 × 21600), 5760 × 2880 for LOLA (original 46080×23040), and 5760×2880 for MOLA (original 46080×23040).
There is already evidence that the Martian channels were formed by a series of fluvial and non-fluvial processes  [61]. Furthermore, we found evidence that the obtained perimeter and area exponents are independent of the resolution factor r (See SI). It is known that the effects of finite size are attenuated as the system size increases. We also obtained similar results for other values of h * on Earth, Moon and Mars. In Fig. 5 , we show the Amazon basin and its associated anti-basins defined by our algorithm on GEBCO dataset at the original resolution. In this special case, we are removing all internal sinks from the South American continent, i. e. we are allowing the existence of sites with negative heights within South America. We emphasise that several rivers (the deeper ones) follow the anti-watershed lines.
This scaling law establishes the relation between the areas (A) of the basins and the maximum lengths ( ) of their rivers, i. e. ∝ A γ , (1) where γ is known as the Hack exponent. In Figure  6 , we show Hack's law for the original orientation of the terrestrial landscape considering only the basins with area greater than A * = 100 km 2. We chose an area threshold A * to ensure that the analyzed basins were not too much affected by the resolution of the dataset. We found the Hack exponent γ = 0. 54 ± 0. 01 with coefficient of determination R 2 = 0. 87, which is very close to our theoretical value of γ ≈ 0. 55 for Earth.
IV. DISCUSSION We proposed a general model to fully delineate multiple drainage basins for any given landscape of heights through an extension of the IPBA. The novelty of our approach is to characterise all basins from a single height dataset through the definition of a reference (sea) level. Such fact allows us to claim that our model is free of parameter tuning. In this way, we are able to delineate the basins through the definition of the watershed network (maximal lines of a landscape) as well as the anti-basins through the definition of the anti-watershed network (minimal lines of a landscape). In order to show that our algorithm was robust, we applied it to real and artificial landscapes. In both cases, we found that the perimeter and area distributions are ruled by power laws with exponents α and β, respectively.
Real Landscapes We use three different DEM datasets throughout this study. The first dataset is the General Bathymetric Chart of the Oceans (GEBCO)  [49]  consisting of altimetric and baltimetric heights, i. e. the heights above and below the sea level, around the Earth globe. The resolution of this dataset is 30 arc-seconds (30/3600 decimal degrees) in both coordinates, equivalent to a square lattice with edge length of 0. 926 kilometers (km) at the Equator line. The other two are the Lunar Orbiter Laser Altimeter (LOLA)  [50]  and Mars Orbiter Laser Altimeter (MOLA)  [51]  consisting of the map of heights for the Moon and Mars, respectively. The LOLA resolution is about 0. 118 km, while MOLA resolution is 0. 463 km, both in relation to their corresponding "Equator". We show the three datasets in Figs.
2A-C. In addition, we point out that all datasets are freely available and are friendly ready-to-use, i. e. all technical preprocessing steps were already performed by the GEBCO, LOLA, and MOLA research teams. We do not perform any additional preprocessing (such as any hydrologic correction or void filling), except the addition of a tiny noise in the heights (< 0. 001 m, much less than the precision of all datasets) in order to ensure that all values in real landscapes are different, defining a unique sequence if the height values are sorted. We also emphasise that all three datasets are available in the Geographic Coordinate System (GCS), more precisely, in latitude and longitude grids in the image format TIFF, i. e. they are mapped on spheres, the terrestrial (with radius R earth = 6378.
Similar Neural Dynamics Reflect the Processing of Touch Location on a Tool and Arm We used multivariate analyses to better test the hypothesis that the brain uses similar processing stages for localizing touch on the arm and tool. First, using a representational decoding approach  [34] , we investigated whether the suppression observed in the arm dataset could predict the suppression observed in the tool dataset. We found statistically significant cross-surface decoding from cortical activity over sensorimotor channels starting within 52 ms of touch on either surface (p < 0. 001; Figure  3F ); decoding was not possible from channels over temporal cortex. Furthermore, hierarchical clustering grouped neural responses by trial condition (i. e. , same, different location) and not touched surface (Figure  3G ).
Electroencephalographic (EEG) recording parameters EEG data were recorded continuously using a 65 channel ActiCap system (Brain Products). Horizontal and vertical electro-oculograms (EOGs) were recorded using electrodes placed below the left eye, and near the outer canthi of the right eye. Impedance of all electrodes was kept at < 20 kU. FCz served as the reference during recording. EEG and EOG signals were low-pass filtered online at 0. 1 Hz, sampled at 2500 Hz, and then saved to a disk. Preprocessing of the EEG data EEG signals were preprocessed using the EEGLab Toolbox  [49]. The preprocessing steps for each participant were as follows: We appended all four blocks of the experiment into a single dataset, resampled the signal at 250 Hz, and high-pass filtered at 1 Hz. We then epoched the data into a time window of 3 s, 1 s before and 2 s after the hit (time zero).
1 trials in Experiment 2 (range:. Next, we used the EEGLab function pop_reref to add FCz (the online reference) back into the dataset. Finally, we re-referenced the data to the average voltage across the scalp and re-epoched the data into a time window of 250 ms, -100 ms before and 150 ms after the hit. We chose this time window because it captures three mid-latency somatosensory evoked potentials (SEPs) related to the early stages of perceptual processing  [22] , the P50, N80 and P100.
The signal was digitized with a 14-bit resolution and sampled at a frequency of 2 kHz over a three-second window using a data acquisition device (National Instruments; Model USB-6009). We then converted the signal into three temporal derivatives (acceleration, velocity, and displacement) and high-pass filtered it at 100 Hz using a 3 rd order zero-phase Butterworth FIR filter. All data was recorded and processed using MATLAB 2017b (The MathWorks).
Skin-neuron model Pacinian mechanoreceptors in the hand have previously been proposed to play a role in encoding vibrations during tool-surface contact  [31]. We recently provided evidence, using a biologically plausible computational skin-neuron model (TouchSim;  [8] ), that they re-encode where a tool has been touched  [7]. MATLAB code to implement the model is freely available online (http:// bensmaialab. org/download/). The reader should refer to the original article for an in-depth treatment of the methods and model validation. We used TouchSim to simulate the responses of a population of Pacinian mechanoreceptors (286 in total) to the vibrations described above. The population was confined to skin surfaces that are tangential to the force of impact when holding the rod. The vibration on each trial (see above) was used as the input for each mechanoreceptor in the population.
QUANTIFICATION AND STATISTICAL ANALYSIS Cluster-based analysis of EEG signals The goal of each experiment was to identify time windows where the amplitude of SEPs was modulated by whether the location of contact was repeated or not (see Main Text). We hypothesized that the evoked response to the second hit would be smaller when the stimulus was at the same location as the first (i. e. , 'same' condition), compared to when it was at a different location (i. e. , 'different' condition). We therefore focused our analysis on the evoked responses to the second stimulus presentation. Identical results were found when this procedure was performed on the two contact locations separately (data not shown); we therefore combined them into a single dataset for the final analysis. To identify time windows of suppression, we used a nonparametric cluster-based permutation test  [55] , a popular data-driven approach that robustly controls for the multiple comparison problem inherent in M/EEG analysis.
Multivariate pattern decoding of EEG signals Decoding was performed on the datasets from the ten participants that took part in both Experiment 1 and 2. The trial-by-trial EEG signal is dominated by low-frequency oscillations, which swamp the evoked responses. Therefore, in order to place greater emphasis on the evoked SEPs, we subtracted the EEG following the first hit from the second hit on every trial. Trials were then classified by the two above-mentioned trial conditions: Same and Different.
Representational distance approach To test the hypothesis that similar processing stages are used to localize touch on an arm and tool, we compared the similarity of the multivariate neural patterns of suppression for both surfaces at each time point (À40 to 120 ms after touch) using the Mahalanobis distance (MD) metric  [34]. All trials in both the tool and arm datasets were divided into the conditions 'same location' and 'different location'. Then, for each trial in the participant's tool dataset, we calculated its MD from the average of all trials in the participant's arm dataset whose condition was either matching or non-matching. For example, a trial in the 'same' condition from the tool dataset could be compared against the average of the 'same' condition (match) or the 'different' condition (non-matching) in the arm dataset.