dataesr/scientific-paragraphs-categorization huggingface dataset:
Data on species commonness We considered popularity (i. e. the number of users) of each distro as a measure of species commonness, which is analogous to either species abundance or range size. However, because most GNU/Linux operating systems are freely available for download and use, there is no single way to directly determine how frequently each distro is used. Therefore, we used three sources of data as proxies of commonness. 1) We used popularity metrics from Distrowatch (www. distrowatch. com), measured as hits per day (HPD), which is the daily number of clicks on each distro-specific page on Distrowatch. We assume that HPD correlated to the actual number of users, i. e. to the number of individuals in the global population of a particular Linux species. Although this assumption is problematic  (Edge 2011) , we still consider the HPD worth exploring, but we also consider alternative measures.
Ecological terminology Linux definition Species or lineage A Linux distribution, most commonly referred to as 'distro'. A distro is a computer operating system comprised of a collection of software packages. Phylogeny Genealogical relationships between distros. Most Linux distros have evolved from one of three main distros: Debian, Red Hat or SLS. We are not aware of a merging between distros, and hence a tree seems to be a good representation of Linux evolutionary history. Commonness (abundance, range size) Hits per day (HPD) or number of machines that run a distro. HPD is a yearly average of the number of times per day any given distro page on DistroWatch. com is accessed. HPD is a proxy for distro popularity. and 28/04/2015 taken from  http://goo. gl/hMjUXr . These data were used to examine the species-abundance distributions (SAD), and to assess the relationship between niche breadth and commonness.
We also extracted HPD for each year between (and including) 2002 to 2015 (from the main page on  www. distrowatch. com , section Page Hit Ranking; downloaded on 28/04/2015). In each year, these data were available only for the 100 most popular distros. This makes them unsuitable for any comprehensive temporal dynamics of SAD, but it allows analyses of population dynamics for the more common distros. We used distros that were present in the data for  10 consecutive years to assess the temporal mean-variance scaling (Taylor's power law; TPL). 2) As an alternative and a more direct measure of distro commonness we used the sample of 164,726 computers registered, by volunteers, at LinuxCounter ( www. linuxcounter. net/statistics/distributions ; downloaded on 27/11/2017), where the identity of each distro on each computer is known.
Models of species-abundance distribution (SAD) We fitted the lognormal and logseries models to the data on frequency of commonness from Distrowatch (HPD) and LinuxCounter (no. of machines). We chose these models since they describe well the generally observed excess or rare species in ecological abundance data  (Baldridge et al. 2016). We fit the logseries model using package sads in R (function fitls). To fit the lognormal model, we used the mean and standard deviation of log commonness as the maximum likelihood estimates of parameters of the lognormal probability density function. To plot the fitted models alongside the data, we drew a random sample from each distribution and ordered the outcomes. This was repeated 500 times and the average is shown as the solid lines in Fig. 1. HPD stands for 'hits-per-day' on www.
Data on niche breadth Niche and niche breadth can be complex concepts  (Chase and Leibold 2003, Peterson et al. 2011 ), here we use a simplified definition of niche breadth as the suite of environments or resources that a species can inhabit or use  (Gaston et al. 1997). We used three proxies of niche breadth of Linux distros. 1) Number of packages. We made the analogy between species functional traits and software packages that come pre-installed with each distro on an installation medium, for example as an. iso file on a live DVD. We defined the number of software packages as niche breadth -more packages mean wider niche breadth. The number of packages was extracted for each distro listed at  www. Distrowatch. com  between 28/04/2015 and 27/04/2016. Distrowatch provides detailed information on each distro, from which we extracted the full list of packages (Ubuntu-specific example:  http://goo.
gl/VDMUt6 ). 3) Number of special applications. In many biodiversity datasets, patterns are mainly influenced by a small number of common species, which can hide potentially interesting patterns of the rare species  (Jetz and Rahbek 2002). We applied this concept here and used the same number of applications as above, but excluded the prevalent 'Desktop' and 'Live CD' categories, so that the number reflects only the relatively narrow applications. In total, we obtained the data for 275 distros, from which we further removed operating systems based on BSD, rolling release distros (e. g. Arch Linux), distros with more than 8000 packages, and the Debian distro. This was because package lists for these distros on Distrowatch show all available packages in their repositories, not packages that are bundled on installation media (L.
Relationship between niche breadth and commonness We fit a generalized linear model (quasipoisson family, log link function) with commonness as a response and the three measures of niche breadth (described above) as predictors; we log-transformed the number of packages prior to the modeling. For commonness, we used both the HPD measure obtained from Distrowatch (227 data points) and the number of machines with a given distro obtained from Linux-Counter (108 data points).
Phylogenetic information The majority of current GNU/Linux distros descended from three original distros: Debian, Red Hat and Soft Landing Linux system (SLS, later known as Slackware). Family trees of these three Linux families were compiled by the GNU/ Linux Timeline project (GLDT) consisting of A. Lundqvist, D. Rodic, M. A. Mustafa, A. Urosevic and J. A. Sandoval; the coded trees are available at  http://futurist. se/gldt/  under GNU Free Documentation Licence. These trees were last updated in 2012, which is also the point at which our phylogenetic analyses terminate. We converted the family trees into Newick phylogenetic trees, and for each distro we extracted the date at which its development began ('speciation'), and the date at which development ceased ('extinction'). Here we rely on the dates provided by the GLDT project, which does not consider the possibility of renewed development after a 'dormancy' period.
Although such 'de-extinctions' can happen in theory, they either did not occur, or were not coded in our three Linux phylogenies. Further, the GLDT estimates of the last development dates may be imprecise -here we assume that the imprecision is either lower, or comparable with, the imprecision of extinction dates derived from fossil data  (Wang and Marshall 2016)  or phylogenies  (Morlon 2014) , which itself can be substantial.
Speciation and extinction through time We separated the process of diversification among GNU/ Linux distros into speciation and extinction. For each of the three distro families we plotted: 1) the cumulative number of speciation events, which is the number of distros that had been created up to a given date (even if development had ceased) -analogous to cumulative speciation through time plots created for biological systems, the so-called 'lineage-through-time plots'  (Nee et al. 1994). 2) The cumulative number of extinction events through time, which is the cumulative number of distros that had ceased development. 3) Per-species instantaneous speciation and extinction rates; these are numbers of distros that were created or their development ceased in a given month, divided by the total number of all extant distros in that month.
Macroecological patterns Species commonness measured as the number of machines or as HPD, followed a skewed frequency distribution (Fig. 1 ), with many distros being uncommon and few distros being common, which is typical for ecological abundance data. In both metrics, the distribution can be approximated by a lognormal probability density function, which performed better than the logseries model. Although the distribution of HPD lacked the typical tail of singletons (Fig. 1d ), this is likely because Distrowatch does not keep track of the extremely rare distros -in this respect the data from LinuxCounter are more representative. We found that the relationship between commonness mean (M) and variance (V) can be modelled by a power function in all three examined datasets (Fig. 2 ), with slope of approximately 2 (Table  2 ).
Macroevolutionary patterns In all three Linux families (i. e. larger groups of Linux distros with the same origin), the cumulative number of phylogenetic lineages increased linearly through time (Fig. 4a, b ). 1. In the Debian family, there was a peak in instantaneous per-species speciation rate around 2005, followed by a pronounced slowdown and a peak of extinction rates with a subsequent slowdown in 2006 (Fig. 4c ). Similar patterns occurred in the Red Hat family, but three years earlier (Fig. 4c ). Diversification in all three Linux families always showed a decline after 2005 (Fig. 4d ). However, in Debian and SLS there also was a relatively low rate of diversification rate prior to 2005, while Red Hat underwent a rapid diversification even prior to 2005 (Fig. 4d ). Our analyses of phylogenetic signal in trait composition showed that more closely related distros (measured by temporal distance from their nearest common ancestor) were more similar in their composition of traits measured by beta-diversity of package composition (Fig.
Discussion Our analyses demonstrate that the Linux universe and biological systems not only showcase many structural analogies (as already pointed out by  Mens et al. 2014a, b) , but also share quantitatively similar emerging properties, including patterns of commonness and evolutionary rates. This is in line with findings from other complex systems, which also exhibit macroecological patterns  (Gaston et al. 1993 , Mace and Pagel 1995 , Bettencourt et al. 2007 , Nekola and Brown 2007 , Warren et al. 2011 , Blonder et al. 2014) ; and macroevolutionary patterns  (Gjesfjeld et al. 2016). The Linux universe shows that not one, but a whole spectrum of biological patterns can emerge in a single non-biological system. Thus, we should expect similar patterns to emerge in other complex systems with categories of objects that evolve, for example in languages, musical styles, political parties, companies, or even countries and religions.
At this point the reader might call for a mechanistic model that could replicate, or predict, the patterns across unrelated systems. Although aimed at describing the origin of different patterns compared to this investigation, such an approach has been already adopted e. g. by  Gherardi et al. (2013) , where a simple stochastic model was able to predict both distribution of Linux package sizes and mammalian body masses. However, to replicate the multitude of patterns presented here, we would have to attain a degree of sophistication that goes beyond the scope of a single paper. Instead, for now we resort to a parsimonious and non-mechanistic explanation for the prevalence of the patterns. Simply, when systems share structural constraints, such as partitioning of objects to categories (species) across space  (Frank 2009 , Harte 2011) , or when variables in the system interact in similar ways [additively vs multiplicatively;  (Blonder et al.
2014) ], they will exhibit similar quantitative patterns, independent on system identity and its detailed inner working. This follows from both the central limit theorem  (McGill and Nekola 2010)  and the theory of maximum entropy  (Harte 2011 ). Thus, we can explain the emergence of the patterns without any typically biological mechanism such as genetic basis for phenotypic variation, complex species interactions, behavior, or community assembly rules. In fact, all of these mechanisms are mostly absent in the Linux universe, yet the biodiversity patterns emerge anyway, which really points to their non-biological causes. In the following, we discuss each investigated pattern individually.
Macroecological patterns We showed that commonness of GNU/Linux-based operating systems follows both a skewed frequency distribution and a power relationship between mean commonness and its temporal variance. In ecology, species-abundance distributions  (Fischer 1943 , McGill et al. 2007 , Nekola and Brown 2007)  and distributions of range sizes  (Gaston 1996b (Gaston , 2003) )  are often best described by the same skewed lognormal or logseries model  (Baldridge et al. 2016). This pattern emerges when individuals within a population have roughly constant per-individual probabilities of reproducing or dying, which in turn leads to stochastic multiplicative (as opposed to additive) dynamics of population size  (Lande et al. 2003 ). In the Linux universe, these multiplicative dynamics emerge when users install (reproduction) and uninstall distros (death) on computers, and when each user has a roughly constant probability of installing his/her favourite distro on a computer, or abandoning the distro.
Similar dynamics can also produce the mean-variance scaling of population abundance fluctuations known as Taylor's power law (TPL), which we also detected in the Linux universe. TPL generally has a proportion of explained variance of  0. 8 in both single-and multi-species systems  (Taylor and Woiwod 1980 , 1982 , Hubbell 2001) , with the slope typically falling between 1 and 2  (Kendal 2004 , Keil et al. 2010 ). Multiple hypotheses have been suggested to interpret empirical TPL slopes  (Kendal 2004 ). Slopes closer to 1 were linked to reproductive asynchrony  (Ballantyne and Kerkhoff 2007) , species interactions  (Kilpatrick and Ives 2003) , hard upper limits on population size  (Keil et al. 2010) , and even to statistical artifacts  (Kiflawi et al. 2016) , while slopes closer to 2 emerge from simple multiplicative models such as random walk or deterministic chaos  (Perry 1994).
The slopes close to 2 observed here suggests that the temporal dynamics of Linux commonness follow an unbounded stochastic multiplicative model, which is also what we expect to lead to the observed skewed frequency distributions. We detected no, or only very weak, positive relationships between commonness and niche breadth of distros (i. e. the functionality that distros offers), both when measuring it as the number of software packages in a distro or as the number of applications (as stated by the developers). In ecology, such positive correlation between niche breadth and commonness (specifically, geographical range size) is a general pattern  (Slatyer et al. 2013 ). The hypothesized ecological explanation is that by exploiting a greater array of resources and maintaining populations in a wider variety of conditions, species may become more common  (Brown 1984).
However, our findings fail to generalize this ecological relationship to the Linux universe. A possible reason for the absence (or weakness) of the link could be our definition of niche breadth, as the number of software packages that come pre-installed with a distro can be easily expanded. Unlike biological phenotypes, which are usually fixed over ecological timescales, numerous additional packages are available for many distros that can be custom-added at any time, and this likely weakens the strength of using pre-installed software for determining the success of an operating system.
Macroevolutionary patterns Diversification slowdowns are often observed in biological systems  [Rabosky and Glor (2010) ,  Moen and Morlon (2014) , but see  Harmon and Harrison (2015)  and  Graham et al. (2016) ], and can be explained by competition for limited resources or space. Similar reasoning can also explain the diversification slowdowns that we detected in the Linux universe: we suggest that between years 2000 and 2005 Linux users had become comfortable with certain distros that satisfied most of the potential applications and user requirements and therefore diversification slowed down. An alternative explanation can be borrowed from  (Gjesfjeld et al. 2016 ) who observed diversification slowdowns in American automobiles, and who argue that the slowdowns can be explained by the emergence of dominant designs  (Abernathy and Utterback 1978).
As  Gjesfjeld et al. (2016)  explains, when technologies become too specialized, the cost of implementing innovations becomes too high and diversification slows down, leading to long-term dominance of the most successful designs. However, since the community-driven universe of Linux is open and largely free from the usual cost or copyright constraints, we consider this economical explanation implausible, and we tend to lean towards explaining the slowdowns by the ecological mechanism involving saturation of a finite niche space. As mentioned, Linux distros come with diverse functionalities, which are pre-installed in the form of software  packages, and which we have made analogous to functional traits of biological species. We found that more closely related distros were more similar in their composition of traits than randomly selected pairs of distros, and 17 to 23% of individual software packages were significantly phylogenetically conserved.
These results are consistent with a widespread macroevolutionary pattern known as phylogenetic 'signal' or 'autocorrelation' of traits, i. e. the tendency of more closely related species to be ecologically (functionally) more similar than species drawn at random from the phylogenetic tree of closely related species  (Harvey and Pagel 1991, Wiens et al. 2010). Finding a phylogenetic signal in Linux distros is expected. Some degree of functional similarity of a descendant distro with the parent is indeed desired, perhaps by the users (they like the familiar), by the developers (they like to build on what has already been built), as well as by the need to avoid radical changes that cause bugs due to complex package dependencies  (Mens et al. 2014a, b). At the same time, the signal is not particularly strong.
Linux as a model system for biodiversity Being well-documented, Linux may serve as a useful model system for studying certain macroecological or macroevolutionary patterns and dynamics that are particularly hard to  get by, given our limited capacity to document biological processes across space, time, and taxa  (Hortal et al. 2015 , Meyer et al. 2015) , and the difficulty of conducting experiments over large spatial and temporal scales. For instance, we unveiled slowdowns in both diversification and extinction rates in Linux, processes that happen at timescales that are impossible to directly observe in nature, and usually need to be indirectly inferred using strong assumptions and complex models. The potential use of the Linux universe as a model for biodiversity may indeed extend beyond the comparisons made here.
Here, we have made the first steps, and these initial analogies can be complemented with a more thorough exploration of the Linux system to fully assess their utility for macroecological and macroevolutionary research. More analogies could be made, and patterns analyzed, based on easily retrievable empirical data on Linux. For instance, economic activity tied to Linux applications could serve as an analogy to productivity of geographical areas and allow studying the similarity in productivity-biodiversity relationships  (Currie et al. 2004 ) of the two systems. Another example is the positive relationship between area and speciation rate  (Lomolino et al. 2010 , Wagner et al. 2014 ): populations in larger areas are more likely to become isolated and drift apart genetically, or to encounter a larger variety of selection pressures and undergo adaptive radiations.
Acknowledgements -We are grateful to  Luke Harmon ,  Antonín Macháč  and eight anonymous referees for extensive comments. Ladislav Bodnář  from  www. distrowatch. com assisted with access to the raw data. Funding -This study was conceived at a workshop ( 25-29/4/2016 , Aix-en-Provence, France) organized by  Alison Specht  and the  French Center for Synthesis and Analysis of Biodiversity (CESAB) , together with  Marten Winter  and the synthesis division (sDiv) of the  German Centre for Integrative Biodiversity Research (iDiv) , funded via the  German Research Foundation  ( DFG FZT 118 ). BY benefited from a postdoctoral fund from the  French national research agency LabEx   ANR-10-LABX-0003-BCDiv , in the context of the 'Investissements d'avenir' no. ANR-11-IDEX-0004-02. KSR was supported by  ERC Adv  grant  26055290.
This study concerns 2D and 3D Finite Element Method (FEM) simulation of surface acoustic wave (SAW) sensors using COMSOL Multiphysics software. SAW device has been designed on piezoelectric substrate; 36° rot lithium tantalate (LiTaO3). Simulations were made on well-known structure to ensure the concordance between 2D and 3D models, and to define a 2D one that can account for and predict the electrical behaviour of SAW transducers for the future optimizations. The results show good agreement between numerical simulation and experimental S21 spectra. Accordingly, we can use the 2D built model for simulations intended to optimize the structure of devices, mainly for increasing their sensitivity.
Results and Discussion 3D Finite Element Method (FEM) simulation was considered to simulate the physical and electrical behavior of SAW delay-line, using COMSOL software. The frequency response is obtained by considering the Delta-Function model whose equations are implemented in COMSOL. The key required parameter is the SAW velocity, that defined the delay-line operating frequency. The simulations are run from 80 MHz to 200 MHz with a frequency step of 1 MHz. Calculated and experimental frequency responses (S21 spectra) are plotted in Fig. 1a  and Fig. 1b  respectively. Numerical simulation results show that the delay-line operates around 104 MHz as expected, but the maximum gain value (-55 dB) is lower than the experimental one (-18 dB). This is due to the fact that only 2 pairs of double fingers for both input and output interdigital transducers (IDTs) were used with COMSOL Multiphysics, while the real SAW device is constituted of 30 pairs fingers for each IDT.
Conclusions In this work, we have used COMSOL Multiphysics software to design and optimize the geometry of a SAW delay-line, and to determine its frequency response (S21 spectra). We have shown that 2D calculation can be used instead of 3D one without any information loss, leading to an important time saving. We observed a good correlation between 2D simulation and experimental results. This is of importance as it permits to envisage modelling of any structure before realization steps. This pad the way for new optimized designs of sensors, including adjunction of suitable materials between input and output IDTs, to confine the SAW energy close to the surface, enhancing thus their sensitivity.
To compute the credit spreads, the harmonised long-term interest rates of government bonds have been collected from the Statistical data Warehouse of the European Central Bank (ECB) website on a daily scale. In particular, they consist of interest rates on sovereign debt securities with maturity of 10 years. The horizon time ranges from 1/1/1998 to 31/5/2017 for the countries which found the European Union (EU). While for the rest of the dataset it depends on the entry date of the countries in EU, i. e. , 1/5/2004 for Latvia, Lithuania, Malta, Poland, Czech Republic, Slovakia, Slovenia, Hungary, 1/1/2007 for Bulgaria and Romania and 1/7/2013 for Croatia Figure  1  shows the evolution of the total cost of sovereign debt paid by the member states in the left panel and the minimum value useful to assess the credit spreads in the right panel.