dataesr/scientific-paragraphs-categorization huggingface dataset:
FUNDING The first workshop in Bergen was additionally funded by the  K. G. Jebsen Centre for Deep Sea Research and the University of Bergen. The second workshop was supported by  ISblue  project , Interdisciplinary graduate school for the blue planet  ( ANR-17-EURE-0015 ) and co-funded by a grant from the  French government  under the program " Investissements d'Avenir ". Additional funding was provided by  Ifremer , and the deṕartment du Finistère. The operation and maintenance of the EMSO-Azores observatory is funded by the by the  EMSO-FR Research Infrastructure (MESR) , which is managed by an Ifremer-CNRS collaboration. The operation and maintenance of the Endeavour observatory is funded by the  Canada Foundation for Innovation's Major Science Infrastructure program  and the  Department of Fisheries and Oceans (Canada).
The operation and maintenance of the Axial Seamount observatory is funded by the  National Science Foundation  as part of the  Ocean Observatories Initiative Regional Cabled Array. MM, JS and PMS acknowledge funding from the  EU   Horizon 2020 iAtlantic project  (Grant Agreement No. 818123 ). AC was supported by the  Operational Program AZORES 2020 , through the Fund  01-0145-FEDER-1279 000140  " MarAZ Researchers: Consolidate a body of researchers in Marine Sciences in the Azores " of the  European Union. She was also supported by  FCT -Foundation for Science and Technology, I. P. , under the project  UIDB/05634/2020  and  UIDP/05634/2020  and through the  Regional Government of the Azores  through the initiative to support the  Research Centers of the University of the Azores  and through the project  M1.
Because these two factors depend on the model to be simulated, which algorithm one should select depends on the model as well as personal preference. We first provide a brief history of the Gillespie algorithms (Section 3. 1). We then introduce the first reaction method (Section 3. 2) because it is conceptually the simpler of the two, followed by the direct method (Section 3. 3), which builds on elements of the first reaction method but makes use of the superposition theorem (Box 1) to directly draw the waiting time between events. We end this section with example implementations in Python of the stochastic SIR dynamics.
There is no event on the timeline of  6. This is because  6 is in the recovered state and no longer undergoes any state change; therefore the event rate of the Poisson process associated with  6 is equal to 0. The first reaction method generates a putative waiting time until the first event for each process and selects the smallest one. The direct method directly generates the waiting time until the first event in the superposed process (bottom of panel (b)). language has a function to generate uniform (pseudo) random variates. We do however advise against using a programming language's standard pseudorandom number generator, which is typically of poor quality. You should instead use one from a scientific programming library or code it yourself. We will discuss some good practices for pseudo-random number generation in Section 4.
Codes Here we present Python codes for the two classic Gillespie algorithms for simulating the SIR model. Then, we compare their output and runtimes to each other and to the rejection sampling algorithm presented in Section 2. 8. We first show codes for the SIR model in a well-mixed population (Sec. 3. 4. 1). We then present implementations for the SIR model on a network (Sec. 3. 4. 2), which requires additional bookkeeping to track the dependencies between nodes and the varying number of reaction channels. All our example codes rely on the NumPy library in Python for vectorized computation and for generating pseudo-random numbers. The following code imports the NumPy library and initializes the pseudo-random number generator (see Sec. 4. 9 for a discussion of how to generate random numbers on a computer).
SIR model in well-mixed populations The major part of the codes for simulating the SIR model is identical for the first reaction and direct methods and is simply related to updating and saving the system's state. We thus show only the code needed for generating the waiting time and the next event in each iteration. The following code snippet implements the first reaction method for the SIR model in a well-mixed population: The following snippet implements the direct method: Finally, the following code snippet, which is common to the two methods, implements the state update after the next event has been selected and recalculates the values of the infection and recovery rates: We compare simulation results for the SIR model in a well-mixed population with  = 100 individuals among the rejection sampling, Gillespie's first reaction method, and Gillespie's direct method in Fig.
as well as their event rates. We import NetworkX as follows: import networkx as nx The following code implements the generation of a single event of the SIR model on a network G using the first reaction method. Here, G stores the connections between nodes as well as each node's state (S, I, or R), event rate, and putative waiting time. The following code implements the generation of a single event of the SIR model on a network G using the direct method. Here, G stores the connections between nodes as well as each node's state (S, I, or R) and its event rate. Lines 13-20 implement the selection of the reaction channel that generates the next event using the simple algorithm illustrated in Fig. 8.
) Extensive test suites such as TestU01 (L'  Ecuyer & Simard, 2007)  and Dieharder  (Brown, Eddelbuettel, & Bauer, 2021)  have been developed for testing the statistical quality of PRNGs. An entirely equivalent definition is that any two (good) PRNGs should lead to statistically the same results of the simulations  (Press et al. , 2007). Several fast and high-quality PRNGs are implemented in standard scientific computing libraries. So, using a good PRNG is as simple as importing it from one of these libraries. For example, we obtained all simulations performed in Python that are shown in this tutorial using the 64-bit Permuted Congruential Generator (PCG64)  (O'Neill, 2014) , which is available in the standard NumPy library. For C++ code (see Section 4. 10), we used the Mersenne Twister 19937 algorithm with improved initialization , i.
Codes Here we showcase some example simulations of event-based stochastic processes using the Gillespie algorithms. In practice, in scientific research in which the Gillespie algorithms are used, we often need to exactly run coupled jump processes on a large scale. For example, you may need to simulate a system composed of many agents, or you may have to repeat the same set of simulations for various parameter values to investigate the dependence of the results on the parameter values of your model. In such a situation, we often want to implement the Gillespie algorithms in a program language faster than Python. Therefore, we implemented the SIR model and three other dynamics in C/C++, which is typically much faster than in Python. We use the Mersenne Twister as the PRNG and only implement the direct method.
Our C/C++ codes and the list of edges of the networks used in our demonstrations are available at Github (https://github. com/naokimas/gillespie-tutorial). Figure  14  Time courses of the fraction of the susceptible, infectious, and recovered nodes obtained from two runs of the SIR model. We set  = 0. 6 and  = 3. We used a regular random graph with  = 1000 nodes and the nodes' degree equal to five. In other words, each node has degree 5, and apart from that, we connect the nodes uniformly at random according to the configuration model  (Fosdick, Larremore, Nishimura, & Ugander, 2018). The network is the same for the two runs shown in (a) and (b). Each run started from the same initial condition in which a particular node was infectious and the other  -1 nodes were susceptible.
e. , an individual of species  consumes an individual of species  with rate    ). We provide a code (lotka-volterra-wellmixed. cc) for simulating the stochastic single-prey single-predator Lotka-Volterra dynamics in a well-mixed population. Two sample time courses of the number of rabbits and that of foxes are shown in Fig. 19. In both runs, the initial condition was the same, i. e. ,  rab = 80 rabbits and  fox = 20 foxes. We see oscillatory behavior of both species with time lags, which is well known to appear in the Lotka-Volterra model. In Fig. 19 (a), the simulation terminated when the rabbits went extinct after two cycles of wax and wane. By contrast, in Fig. 19(b ), the simulation terminated when the foxes went extinct after many cycles of wax and wane. The apparent randomness in the sequence of the height of the peaks in Fig.
Acknowledgments We thank the SocioPatterns collaboration (see http://www. sociopatterns. org)for providing the experimental data set. N. M. thanks the financial support by  AFOSR European Office  (under Grant No. FA9550-19-1-7024 ),  National Science Foundation  (under Grant No. DMS-2052720 ), the  Nakatani Foundation , the  Sumitomo Foundation , and the  Japan Science and Technology Agency  (under Grant No. JPMJMS2021 ). C. L. V. was supported in part by the the  French government  under management of  Agence Nationale de la Recherche  as part of the  "Investissements d'avenir" program , reference  ANR-19-P3IA-0001  ( PRAIRIE 3IA Institute ).
K E Y W O R D S biological invasion, crustacean, freshwater ecosystem, invasive alien species, long-term monitoring, time-series | 3 SOTO et al. The form of population dynamics may differ among invaders, invasion pathways, biogeographical regions, abiotic or biotic gradients and spatio-temporal scales  (Arim et al. , 2006; Haubrock et al. , 2020). Characterization of population dynamics of invasive alien species at a broad spatio-temporal scale has been limited by insufficient long-term data. However, advances in availability in long-term biodiversity monitoring  (Dornelas et al. , 2018; Mirtl et al. , 2018)  and data analysis approaches for biodiversity time series  (Bowler et al. , 2017; Dornelas et al. , 2014; Pilotto et al. , 2020)  have been made. These and other initiatives have collated large datasets for diverse taxonomic groups and examined biodiversity and ecosystem function trends at regional and global scales  (Seebens et al.
, 2017)  and therefore be expanding to new areas more slowly than populations with a more recent invasion history  (Seebens et al. , 2018). More recent invasions may be at an early stage of spread, potentially preceding future exponential population increases. In addition, invasion success and exerted impacts are intertwined with anthropogenic stressors such as hydromorphological alterations and dam construction, which can influence spread rates  (Colautti et al. , 2006; MacNeil & Platvoet, 2013). To characterize the population dynamics of D. villosus across time series, we collated European riverine macroinvertebrate biomonitoring data containing this species. We hypothesized that the.
| Data compilation To investigate the population dynamics of D. villosus and the response of recipient communities across Europe, we considered 1816 time series (Peter Haase, unpublished data) reporting the abundance of macroinvertebrate taxa in streams and rivers across 22 European countries. Macroinvertebrates were sampled using different methods and protocols (see Table  S1 ) among time series, but were consistent within each time series. Each time series comprised macroinvertebrate assemblages collected at a single site in multiple years. We initially selected all 132 time series with D. villosus populations and then excluded 36 time series that contained ≤2 sampled years, retaining 96 time series (Figure  1 ), of which most are from large European rivers (e. g. Danube or Rhine) across six European countries (i.
| Trend identification and metaregression modelling To synthesize and describe the directionality and the trends in the number of sampled D. villosus individuals, we used a metaregression modelling approach, which synthesizes the slopes of individual regressions (as in our case from different time series). For this, we used the "rma. mv" function of the R package metafor  (Viechtbauer, 2010) , using the time-series Mann-Kendall trend test (S-statistics) and respective variance as the effect size. The Mann-Kendall trend test is a non-parametric test to evaluate a monotonic increase or decrease in trends. In particular, we used the trends calculated by modified Mann-Kendall trend tests with variance correction to account for temporal auto-correlation  (Hamed & Rao, 1998; Maire et al. , 2019).
| Effects of site-specific characteristics on D. villosus To investigate the spatial and climatic drivers of D. villosus trends, we used site-specific characteristics of each time series. Climatic regions (i. e. Köppen-Geiger climate zone) were extracted from  Beck et al. (2018). Biogeographical regions were defined following the European Environment Agency classification map and estimated visually using the site-specific coordinates (EEA, 2021). We further classified the type of ecosystem of each time series based on Strahler order (i. e. stream <8 or large rivers ≥8) to evaluate differences in the degree of invasions between both. We obtained site-specific runoff data, expressed as the annual Q (mm), from the TerraClimate dataset at 4-km spatial resolution  (Abatzoglou et al. , 2018). We extracted the elevation of each site from the MERIT Hydro digital elevation model  (Yamazaki et al.
, 2019)  at 90-m spatial resolution and used the Hydrography90m  (Amatulli et al. , 2022)  stream network, catchments and sub-catchments (catchments between network nodes) as underlying spatial units. For each site, we computed the stream slope using the r. stream. slope function. We extracted land cover data from the European Space Agency Climate Change Initiative (ESA CCI) Land Cover time series v2. 0. 7 dataset, at 300-m spatial resolution  (ESA, 2017) , as the percentage cover of a given land cover category within the sub-catchment. We used the Global Reservoir and Dam Database (GRanD) v1. 3 to identify dams along the river network. We then measured the distance to the nearest dam to investigate the effect of instream barriers. We extracted mean daily temperature and total daily precipitation data from a gridded European scale observation-based dataset (spatial resolution: 0.
villosus' dominance in invaded ecosystems over time) were analysed as a function of these spatial, temporal, and site-specific characteristics, to identify significant drivers of temporal trends (see Table  S2 ). We used generalized linear models (GLMs) via the MASS R package  (Ripley et al. , 2013). We used a Gaussian distribution for continuous data and quasibinomial distribution (to account for high variance of the dataset) for proportion data (i. e. relative abundances) after visually inspecting their respective residual distributions for normality through histograms. To identify the best model, we first tested for collinearity among the numerical variables using the variance inflation factor (VIF) for continuous predictors using the "corvif" function  (Zuur et al. , 2009). We selected VIF >5 as the threshold, and those variables with high VIF values for each model were assessed for their ecological relevance based on expert knowledge (Table  S3 ;  Dorman et al.
| Modelling occurrence frequency and invasion speed We combined the first occurrence of D. villosus in each time series in our data with those occurrences (as coordinates and year of records) in the Global Biodiversity Information Facility database (GBIF, 2021). Eight GBIF occurrences were removed due to insufficient information (e. g. no recorded year), resulting in 400 records. We excluded those sample years from the dataset of 400 records that reported relatively high occurrence frequencies, that is, any number of occurrences that was greater than Q 3 + 3 × IQR, where Q 3 is the upper quartile and IQR is the interquartile range of the dataset. A single outlier was found with 120 occurrences in the year 2009, and thus, it was removed. We modelled the remaining 280 occurrences to represent invader spread, using a logistic distribution and a two-tailed Pareto distribution.
A key difference between these distributions is the decay rate at the end tails: the logistic distribution decays exponentially fast (thin tails), and for the Pareto distribution, the decay is much slower according to an inverse power law (fat tails; Nolan, 2020). Moreover, Pareto distributions with distinct parameters were considered (i. e. two-tailed) for the early and late phases of the invasion. Both distributions were fitted against the occurrence data using the non-linear regression tool lsqcurvefit in Matlab. The better-fitting model was determined based on lower number of parameters and higher R 2 value (see Note S2). Further, we estimated the frequency of occurrences f 0 at the time of first sampling (t = 0) the time of introduction t intro , and the duration of the lag phase t lag that is, the period before D.
villosus was observed in additional assemblages, evaluated at 10% of the largest recorded occurrence frequency f*. As a complementary analysis, we computed the distance between the locations of every sampled site from the first invaded site using site location data (GPS coordinates recorded as latitude and longitude) over the years 1994-2021. An estimate for the invasion speed (km/ year) was obtained by computing the mean distance (i. e. total distance averaged over the number of occurrences per year) over time and modelled using a linear equation  (Bagnara et al. , 2022). We also calculated the differences between the first record of D. villosus in our data and sTWIST database (the most comprehensive source of first records of alien species, integrating several databases and merging them into a single database;  Seebens et al.
Metrics were calculated using the "diversity" function in the R package vegan  (Oksanen et al. , 2013)  and the "turnover" function in the R package codyn  (Hallett et al. , 2016). In analysing these metrics as response variables in metaregression models, we included the middle point of each time series All models used restricted maximum likelihood estimation (REML). We also quantified the proportion of variance in the model not attributed to sampling error by using the I 2 statistic. In addition, we evaluated the results of the meta-regression by a graphical representation (i. e. forest plots) using the "forest" function of the R package metafor (Figure  S2 ;  Viechtbauer, 2010). To inspect potential biases that may alter the results, we checked the symmetry of the data using funnel plots and statistically evaluated this symmetry using the Egger's test  (Egger et al.
| Trends of D. villosus across Europe Across the sampling sites in Europe included in this study, the abundance trend of D. villosus increased in 49 locations, decreased in 44 locations and has not changed in 3 locations (Figure  2a ). Our analysis therefore suggests that the total number of D. villosus individuals in the study region experienced no overall significant change in its rising trajectory between 1994 and 2019s (S-statistics = 4. 74; CI: -9. 32, 18. 82, p =. 50; Table  1 ; Figure  2a ), albeit expressing low heterogeneity (I 2 = 3. 62%). In addition, we did not find differences in the population growth between earlier (for which t = 0 was before 10. 8 years later than those referenced in the sTWIST database. This difference was reduced to 2. 8 years after excluding time series from Austria and Switzerland (n = 2), for which the difference between both databases was 14 years  (Seebens et al.
, 2018; Warren et al. , 2021). In addition, the difference between the first record of D. villosus in sTWIST and our database was ~10. 8 years. Yet, after excluding Switzerland and Austria-which had a difference of ~14 years between both databases likely due to the scarce time series from that country (n = 2)-the difference among both databases shrank to ~2. 8 years only, underlining the accuracy of our data.
| Implications and conclusions The scope of our study was limited by the time series represented in our database. Invasive alien species, such as D. villosus, can affect food webs through either bottom-up or top-down regulation, potentially triggering trophic cascades that cause major disturbances in invaded ecosystems  (Van Riel et al. , 2006). The greater effects of D. villosus following steeper increases in its abundance suggest that the main effects on invaded communities are driven by high population densities, which overwhelm the ecological resistance of the recipient community. Surprisingly, the data from GBIF and our time series do not overlap in some cases, highlighting areas for future abundance survey efforts. Dikerogammarus villosus was first recorded in Italy in 1992  (Seebens et al.
The lefthand edge of the 2D map is required to be at 0°C arrington longitude. 2. A 2D map of the position of the HCS at the outer boundary of the model binned in the same way as the coronal hole map. Pixels have the value of +1 for locations where the field is directed outwards (positive polarity) and -1 where the field is directed inwards (negative polarity). Presented in this way, the HCS can be extracted from the map using contouring software in IDL or Python at the zero contour level, which allows for the possibility that the HCS consists of multiple closed loops. Although such topologies were not encountered in this study due to the deep solar minimum, this allows this framework to be useful at future (or previous) solar maxima, where such a topology may occur. Thus, we require models to predict coronal structure at both their inner and outer boundaries.
EUV Coronal Hole Synoptic Maps We use SDO Atmospheric Imaging Assembly (AIA;  Lemen et al. 2012)  observations to prepare our EUV Carrington maps from which coronal holes can be detected. We downloaded 180 s worth of data for the 304 Å, 171 Å, 193 Å, and 211 Å filters each day of the Carrington rotations relevant to PSP encounters 1-3. The data were processed using standard AIA analysis software. We performed a deconvolution of the pointspread function on each image using deconvolve from aiapy 17  and produced a median-filtered composite image in each wavelength for each day. The daily images were converted from helioprojective coordinates to Carrington longitude and sine latitude using wcs_convert_to_ coord, 18  a routine in the Solar Software (SSW) IDL package  (Freeland & Handy 1998). Finally, these are combined to produce an EUV Carrington map for the Carrington rotation.
2016) , and applying intensity thresholding across multitemperature image passbands  (Garton et al. 2018). Each of these techniques has its advantages and weaknesses. Automatic detection algorithms, for example, are very powerful for processing large volumes of data. In this work, we experimented with several methods: simple EUV intensity thresholding, the coronal hole identification via multithermal emission recognition algorithm (CHIMERA;  Garton et al. 2018) , and the Predictive Science Inc. (PSI) EZSEG image segmentation code  (Caplan et al. 2016 ). Ultimately we chose to use the EZSEG algorithm. For this work, the chosen algorithm needed to be able to accurately produce contours of coronal holes from 2D Carrington maps to allow direct comparison to our chosen observables (see Section 4.