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Context 1: Paper Info

Title: Nuclear Liquid-Gas Transition in the Strong Coupling Regime of Lattice QCD
Publish Date: 28 Mar 2023
Author List: J Kim (from Institute for Advanced Simulation (IAS-4), Forschungszentrum Jülich), P Pattanaik (from Fakultät für Physik, Bielefeld University), W Unger (from Fakultät für Physik, Bielefeld University)

Figure

FIG  1 Typical 2-dimension configuration at β = 1 0, at non-zero quark mass, temperature, chemical potential The black dots are monomers, the blue lines are dimers, the red arrows are baryon loop segments (or triplets g b + f b = ±3 if adjacent to a non-trivial plaquette), and the green squares are plaquette occupations ±1 The actual configurations are 3+1-dimensional 
FIG 2 Chiral susceptibility on a 2 4 volume for various quark masses, as a function of the bare anisotropy γ (with aT = γ 2 /2), analytic results from enumeration compared to numerical data from simulations via the worm algorithm 
FIG 3 Various observables in the µB-T plane on a 2 4 volume at amq = 0 1 The back-bending of the first order transition at temperatures below aT = 0 5 in all observables is an artifact of the small volume, and vanishes in the thermodynamic limit The temperature aT = 1/2 corresponds to the isotropic lattice here 
FIG  4  The chiral condensate (left) and the baryon density (right) for quark mass m = 1 5 as a function of the chemical potential and for various temperatures 
FIG  7  ∆f at amq = 0 2 as a function of chemical potential and β the on a 6 3 × 4 lattice
FIG  8  Baryon mass from ∆E as a function of the quark mass amq, and contributions from different dual variables: monomers, dimers and baryon segments 
FIG  9  Baryon density for volume 4 3 × 8 in the full µB − mq plane, illustrating the strong quark mass dependence of the onset to nuclear matter 
FIG  10 Baryonic observables on various volumes in the first order region amq = 1 5 Vertical bands indicate the mean and error of the nuclear transition 
FIG  12  Left: Extrapolation of the pseudo-critical values of µB for the various volumes into the thermodynamic limit Right: Critical baryon chemical potential for different quark masses The first order transition region is shown in blue, the crossover region is shown in red and the range for critical end point is marked in black 
FIG  17  Nuclear interaction scaled with baryon mass As the quark mass increases, it tends to zero 
FIG  18  Critical baryon chemical potential and baryon mass from different approaches 
Parameters for the Monte Carlo runs to determine the nuclear transition at strong coupling, with statistics after thermalization 

abstract

The nuclear liquid-gas transition from a gas of hadrons to a nuclear phase cannot be determined numerically from conventional lattice QCD due to the severe sign problem at large values of the baryon chemical potential  In the strong coupling regime of lattice QCD with staggered quarks, the dual formulation is suitable to address the nuclear liquid gas transition 
We determine this first order transition at low temperatures and as a function of the quark mass and the inverse gauge coupling β  We also determine the baryon mass and discuss the nuclear interactions as a function of the quark mass, and compare to mean field results  It is known from experiments that at low temperatures, there is a phase transition between dilute hadron gas and dense nuclear matter as the baryon chemical potential increases 
This transition is of first order and terminates at about T c = 16 MeV in a critical end point  The value of the chemical potential µ 1st B at zero temperature is given roughly by the baryon mass m B , where the difference of µ 1st B −m B is due to nuclear interactions  For a review on nuclear interactions see  
As the nuclear force between baryons to form nuclear matter is due to the residual strong interactions between quarks and gluons, it should be accurately described by QCD  We choose to study the nuclear transition and nuclear interaction via lattice QCD , with its Lagrangian being a function of the quark mass and the inverse gauge coupling 
In order to understand the nature of the transition, it is helpful to study its dependence on these parameters  However, at finite baryon density, lattice QCD has the infamous sign problem which does not allow us to perform direct Monte Carlo simulations on the lattice  Various methods have been proposed to overcome the numerical sign problem, but they are either limited to µ B /T 3 or can not yet address full QCD in 3+1 dimensions in the whole µ B − T plane , in particular the nuclear transition is out of reach 
An alternative method is to study lattice QCD via the strong coupling expansion  There are two established effective theories for lattice QCD based on this: (1) the 3-dim  effective theory for Wilson fermions in terms of Polyakov loops, arising from a joint strong coupling and hopping parameter expansion , the dual representation for staggered fermions in 3+1 dimensions, with dual degrees of freedom describing mesons and baryons 
Both effective theories have their limitations: is limited to rather heavy quarks (but is valid for large values of β) whereas ( ) is limited to the strong coupling regime β 1 (but is valid for any quark mass)  We study lattice QCD in the dual formulation, both at infinite bare gauge coupling, β = 0, and at leading order of the strong coupling expansion in the regime β < 1, which is far from the continuum limit 
But since strong coupling lattice QCD shares important features with QCD, such as confinement, and chiral symmetry breaking and its restoration at the chiral transition temperature, and a nuclear liquid gas transition, we may get insights into the mechanisms, in particular as the dual variables give more information in terms of its world lines, as compared to the usual fermion determinant that depends on the gauge variables 
To establish a region of overlap of both effective theories, we have chosen to perform the Monte Carlo simulations in the dual formulation extending to rather large quark masses  This paper is organized as follows: in the first part we explain the dual formulation in the strong coupling regime, in the second part we provide analytic results based on exact enumeration and mean field theory, in the third part we explain the setup of our Monte Carlo simulations and present result on the m q -and β-dependence of the nuclear transition 
Since the strong coupling regime does not have a well defined lattice spacing, we also determine the baryon mass am B to set the parameters of the grand-canonical partition function, aT and aµ B , in units of am B   We conclude by discussing the resulting nuclear interactions, and compare our findings with other results 

Staggered action of strong coupling QCD and its dual representation

In the strong coupling regime, the gauge integration is performed first, followed by the Grassmann integration to obtain a dual formulation  This was pioneered for the strong coupling limit in and has been extended by one of us to include gauge corrections   The sign problem is mild in the strong coupling limit and still under control for β < 1, where we can apply sign reweighting 
The dual degrees of freedom are color-singlet mesons and baryons, which are point-like in the strong coupling limit, and become extended about a lattice spacing by incorporating leading order gauge corrections  The partition function of lattice QCD is given by where DU is the Haar measure, U ∈ SU(3) are the gauge fields on the lattice links (x, μ) and { χx , χ x } are the unrooted staggered fermions at the lattice sites x 
The gauge action S G [U] is given by the Wilson plaquette action and the staggered fermion action S F [ χ, χ, U] is: where the gauge action depends on the inverse gauge coupling β = 2Nc g 2 and the fermion action depends on the quark chemical potential aµ q which favors quarks in the positive temporal direction, and the bare quark mass am q  
First we consider the strong coupling limit where the inverse gauge coupling β=0 and hence the gauge action S G [U] drops out from the partition function in this limit  The gauge integration is over terms depending only on the individual links (x, μ) so the partition function factorizes into a product of one-link integrals and we can write it as:
with z(x, μ) the one-link gauge integral that can be eval-uated from invariant integration, as discussed in , where we write the one-link integral in terms of new hadronic variables: Only terms of the form (M (x)M (y)) k x, μ (with k x,μ called dimers which count the number of meson hoppings) and B(y)B(x) and B(x)B(y) (called baryon links) are present in the solution of the one-link integral 
The sites x and y = x + μ are adjacent lattice sites  It remains to perform the Grassmann integral of the fermion fields χ, χ  This requires to expand the exponential containing the quark mass in Eq  (4) (left), which results in the terms (2am q M (x)) nx (with n x called monomers)  To obtain non-vanishing results, at every site, the 2N c Grassman variables χ x,i and χx,i have to appear exactly once, resulting in the Grassmann constraint (GC):
where n x is the number of monomers, k x,μ is the number of dimers and the baryons form self-avoiding loops x,μ , which due to the constraint cannot coexist with monomers or dimers  With this, we obtain an exact rewriting of the partition function Eq  ( ) for N c = 3, in terms of integer-valued dual degrees of freedom {n, k, }:
where the sum over valid configurations has to respect the constraint (GC)  The first term in the partition function is the contribution from dimers and the second term is the contribution from monomers  The weight factor w( ) for each baryon loop depends on the baryon chemical potential µ B = 3µ q and induces a sign factor σ( ) which depends on the geometry of :
Here, ω is the winding number of the loop   The total sign factor σ( ) ∈ {±1} is explicitly calculated for every configuration  We apply sign reweighting as the dual formulation has a mild sign problem: baryons are non-relativistic and usually have loop geometries that have a positive signs  The dual partition function of the strong coupling limit is simulated with the worm algorithm (see Section III A) and the sign problem is essentially solved in this limit 

Extension to finite β

The leading order gauge corrections O(β) to the strong coupling limit are obtained by expanding the Wilson gauge action Eq  ( ) before integrating out the gauge links  A formal expression is obtained by changing the order of integration (first gauge links, then Grassmann-valued fermions) within the QCD partition function:
With this the O (β) partition function is The challenge in computing Z (1) is to address the SU(N c ) integrals that receive contributions from the elementary plaquette U P   Link integration no longer factorizes, however the tr[U P ] can be decomposed before integration: Integrals of the type J ij with two open color indices -as compared to link integration at strong coupling -have been derived from generating functions
for either J = 0 or for G = U(N c )   The SU(3) result was discussed in , in terms of the dual variables, neglecting rotation and reflection symmetries, there are 19 distinct diagrams to be considered  The resulting partition function, valid to O(β), is with q P ∈ {0, ±1}, and the site weights w x → ŵx , bond weights w b → ŵb and baryon loop weights w → ŵ receive modifications compared to the strong coupling limit Eq  ( ) for sites and bonds adjacent to an excited plaquette q P = 1 
The weights are given in , and are rederived for any gauge group in   The configurations {n, k, , q p } must satisfy at each site x the constraint inherited from Grassmann integration: which is the modified version of Eq  ( ) with q x = 1 if located at the corner of an excited plaquette q p = 0, otherwise q x = 0 
A more general expression that we obtained via group theory and is valid to higher orders of the strong coupling expansion is discussed in terms of tensor networks   A typical 2-dimensional configuration that arises at β = 1 in the Monte Carlo simulations is given in Fig    Note that if a baryon loop enters a non-trivial plaquette, one quark is separated from the two other quarks, resulting in the baryon being extended object, rather being point-like in the strong coupling limit 
The O(β) partition function has been used in the chiral limit to study the full µ B − T plane via reweighting from the strong coupling ensemble  Whereas the second order chiral transition for small values of the aµ B decreased up to the tri-critical point, the first order nuclear transition was invariant: aµ 1st B 1 78(1) at zero temperature has no β-dependence 
For the ratio T (µ B = 0)/µ 1st B (T 0) we found the values 0 787 for β = 0 and 0 529 β = 1, which should be compared to T c / 0 165 for full QCD   However, since reweighting cannot be fully trusted across a first order boundary, direct simulations at nonzero β are necessary  The Monte Carlo technique to update plaquette variables is discussed in Section III A 
In this section, we provide analytic results from exact enumeration for small volumes, and mean field results based on the 1/d expansion, valid in the thermodynamic limit  The main purpose is to compare our Monte Carlo results to these analytic predictions 

Exact enumeration

To establish that our Monte Carlo simulations indeed sample the partition functions Eq  ( ) and Eq  ( ), we have obtained analytic results on a 2 4 volume at strong coupling, and at finite beta in two dimensions on a 4 × 4 volume, comparing O (β) and O β 2 truncations  Our strategy to obtain an exact enumeration of the partition function Z is to enumerate plaquette configurations first, then fixing the fermion fluxes which together with the gauge fluxes that are induced by the plaquettes form a singlet, a triplet or anti-triplet, i e  on a given bond b, g b + f b ∈ {−3, 0, 3}, and last we perform the monomerdimer enumeration on the available sites not saturated by fermions yet by a depth-first algorithm  
At strong coupling, with no plaquettes, g b = 0 and f b are baryonic fluxes  All observables that can be written in terms of derivatives of log(z), such as the baryon density, the chiral condensate, the energy density, and also the average sign, are shown in Fig 

Expectations from mean field theory

Another analytical method to study strong coupling lattice QCD is the mean field approach, where the partition function is expanded in 1 d (d is the spatial dimension) and then a Hubbard-Stratonovich transformation performed   After this procedure, the free energy is a function of temperature T , the chiral condensate σ and chemical potential µ B :
here E[m] is one-dimensional quark excitation energy which is a function of the quark mass m = am q   For N c = 3 and d = 3 we determined the minimum of the free energy with respect to the chiral condensate  This gives us the equilibrium chiral condensate as a function of (T, m, µ B )  The chiral condensate and the baryon density as a function of the baryon chemical potential in lattice units aµ B and for various temperatures at quark mass m = 1 5 is shown in Fig    We have determined the critical temperature to be aT c = 0 23 , which is characterized by an infinite slope of the chiral condensate 
For lower temperatures, there is a clear discontinuity of the chiral con-densate, separating the low density phase from the high density phase  For temperatures above and in the vicinity of aT c the chiral condensate and baryon density has no discontinuity but rapidly changes, corresponding to a crossover transition 
With this method, the phase diagram is plotted for different quark masses in Fig    The second order phase transition in the chiral limit is plotted in solid blue line, the dotted lines show the first order phase transition for different quark masses and the solid red line indicates the critical end point for the different quark masses 
Mean field theory also gives an expression for the pion mass am π and the baryon mass am B : The mean field baryon mass for N c = 3, d = 3 is also plotted in red in Fig    Whereas the baryon mass is around N c in the chiral limit (am B 3 12 for N c = 3), it approximately doubles at m = 3 5 (am B 6 28) which corresponds to the pion mass am π = 4 45, i e  m π /m B = 0 708 
Hence, at around bare mass m = 3 5, the valence quark mass of the baryon corresponds roughly to 1/3 of the chiral limit value of the baryon mass  The first Monte Carlo simulations that could extend in the µ B − T plane was the MDP algorithm , but it required the introduction of the worm algorithm to make substantial progress 
First studies of the worm algorithm applied to the strong coupling limit QCD (with gauge group U(3)) are , and for gauge group SU   Monte Carlo simulations to extend the worm to incorporate leading order corrections were first proposed in   We will shortly review the setup of or Monte Carlo strategy for the nuclear transition, with an emphasis on the challenges to address large quark masses 

Strong Coupling

Without any further resummation, there is a mild sign problem in the dual formulation of lattice QCD in the strong coupling limit  When the average sign σ is not too small (close to zero), it implies that most of the configurations have a positive weight thus allowing us to perform sign reweighting strategies 
In Fig  , ∆f is plotted as a function of the baryon chemical potential and the quark masses  It is seen that ∆f is close to zero for most cases except near the critical chemical potential and for small quark masses, but never exceeds 5 × 10 −4   Hence sign reweighting can be performed in the full parameter space 
The result that the sign problem becomes even milder when increasing the mass is related to the fact that larger critical chemical potentials result in a larger fraction of static baryons (spatial baryon hoppings become rare)  FIG    ∆F at strong coupling as a function of chemical potential and quark mass on a 6 3 × 8 
The sign problem becomes milder as the quark mass increases 

Finite β

All runs at finite β have been obtained for N τ = 4, which corresponds to a moderately low temperature aT = 0 25 compared to the value of the chiral transition aT 1 54  Those simulations were too expensive to attempt N τ = 8 runs, in particular as a higher statistics was required  The spatial volumes are 4 3 , 6 3 and 8 3  
For β values are from 0 0 to 1 0 with step size 0 1, and for am q values from 0 00 to 1 00 with step size 0 01  The values of aµ were chosen close to the nuclear transition, the scanning range is shifted to large values as am q increases  At small quark masses the scanning range is from aµ = 0 4 to 1 0 and for the large quark masses, it is from 0 6 to 1 2 with step size 0 01 
The statistics used for are 15 × 10 4 measurements and between measurement, 40 × N 3 s worm updates 

Residual sign problem

Although it is possible to resum the sign problem at strong coupling with a resummation of baryon and pion world lines, this is not possible when including gauge corrections  In order to compare both sign problems, we kept the original dual formulation to monitor the severity of the sign problem  This is done via the relation
between the average sign σ and the difference of the free energy density ∆f between the full ensemble f and of the sign-quenched ensemble f ||  

Nuclear interactions

We have found that aµ 1st B is very different from the baryon mass  This must be due to strong attractive interactions of nucleons  In contrast to continuum physics, in the strong coupling limit there is no pion exchange due to the Grassmann constraint  Instead, nucleons are point like and hard core repulsive 
However, the pion bath, which is modified by the presence of static baryons, results in an attractive interaction  In , this has been analyzed in the chiral limit using the snake algorithm, and it has been found that the attractive force is of entropic origin  Here, we do not quantify the nuclear interaction via the nuclear potential, but via the difference between critical baryon chemical potential and baryon mass, in units baryon mass, as shown in Fig  , given the am B as measured in Section III C 
This compares better to the 3dim  effective theory  The nuclear interaction is maximal and more than 40% in the chiral limit, which is related to pions being massless: the modification of the pion bath is maximal  We clearly find that the nuclear interaction decreases drastically and almost linearly until it almost approaches zero at about am q = 2 0, corresponding to a pion mass am π = 3 36, see Section II B  The large error bars for larger quark masses, that are due to the subtraction of almost same magnitudes, makes it difficult to extract a non-zero nuclear interaction at the largest quark masses 
In this work, we have determined the baryon mass and the nuclear transition via Monte Carlo: the worm algorithm based on the dual formulation, at finite β equipped with additional updates  All those numerical results and various analytic expressions are summarized in Fig    We find that as the quark mass becomes large, spatial mesons hoppings (i e 
spatial dimers) become rare, which makes this 3+1-dimensional system closer to 1dim  QCD   Also, both the baryon mass and the baryon chemical potential obtained in our dual representation, i e  for staggered fermions, approaches the baryon mass of the 3-dim  effective theory which is based on Wilson fermions 
Another comparison that summarizes the validity of the mean field approach discussed in Section II B is shown in Fig    It is evident that mean field theory has strong deviations for small quark masses, but this discrepancy becomes smaller for larger quark masses  The extension of the study of the nuclear transition to finite inverse gauge coupling β is summarized in Fig  , which shows the β-dependence of aµ c B for various quark masses 
For all quark masses ranging from am q = 0 to am q = 1 0, there is only a very weak β-dependence, confirming the expectation from mean field theory   This works was restricted to isotropic lattices ξ = a/a t = 1, i e  we performed simulations at fixed temperature  Non-isotropic lattices are necessary to vary the temperature at fixed values of β 
This requires to include two bare anisotropies, γ for the fermionic action and γ G for the gauge action  Finite β has only been studied by us in the chiral limit   Clearly, it is interesting to study the location of the nuclear critical point also including higher order gauge corrections and at finite quark mass 
Simulations including O(β 2 ) are under preparation 
Context 2: Paper Info

Title: Age and market capitalization drive large price variations of cryptocurrencies
Publish Date: 23 Feb 2023
Author List: 

Figure

Figure 3  Illustration of different effects of age and market capitalization on power-law exponents of cryptocurrencies (a) Posterior probability distributions of the linear coefficients associated with the effects of age [p(A)] and (b) the effects of market capitalization [p(C)] on power-law exponents related to large positive returns Panels (c) and (d) show the analogous distributions for the association with power-law exponents related to large negative returns In all panels, the different curves show the distributions for each of the top 20 cryptoassets by market capitalization Cryptocurrencies significantly affected by age or market capitalization are highlighted in boldface, and the numbers between brackets show their positions in the market capitalization rank 
Figure S5 There is more probability mass in the positive tail than in the negative tail of price returns (a) Probability distributions of the lower cut-offs (r min ) obtained by applying the Clauset-Shalizi-Newman method to positive (blue) and negative (red) returns The vertical dashed lines indicate the median values of r min for positive and negative returns (b) Probability distributions of 90th percentiles (r 90 ) estimated from the power-law models adjusted to positive (blue) and negative (red) returns The vertical dashed lines indicate the median values of r 90 for positive and negative returns (c) Probability distributions of the fraction of weeks that r 90 estimated from positive returns (r + 90 ) is larger than r 90 estimated from negative returns (r − 90 ) This fraction is calculated only for weeks in which the power-law hypothesis is not rejected for both tails The percentage of cryptoassets for which r + 90 > r − 90 is shown in the panels The first column of panels depicts the results when considering data from all cryptocurrencies, while the second and third columns present the results for the top 2000 and top 200 cryptocurrencies by market capitalization, respectively 
Figure S7 Robustness of the results of Fig  2(b)-(d) against considering only cryptocurrencies with fraction of rejection f r < 0 1 Panels (a) and (b) show the same distributions of Fig  S4 but after filtering out all time series of cryptocurrencies with fraction of rejections f r ≥ 0 1 As in the case related to sampling issues, we observe that these distributions barely change when considering only cryptocurrencies with f r < 0 1 Indeed, the distributions in this figure are not significantly distinguishable from their counterparts in Fig  S4 (two-sample Kolmogorov-Smirnov test, p > 0 05) 

abstract

Cryptocurrencies are considered the latest innovation in finance with considerable impact across social, technological, and economic dimensions  This new class of financial assets has also motivated a myriad of scientific investigations focused on understanding their statistical properties, such as the distribution of price returns 
However, research so far has only considered Bitcoin or at most a few cryptocurrencies, whilst ignoring that price returns might depend on cryptocurrency age or be influenced by market capitalization  Here, we therefore present a comprehensive investigation of large price variations for more than seven thousand digital currencies and explore whether price returns change with the coming-of-age and growth of the cryptocurrency market 
We find that tail distributions of price returns follow power-law functions over the entire history of the considered cryptocurrency portfolio, with typical exponents implying the absence of characteristic scales for price variations in about half of them  Moreover, these tail distributions are asymmetric as positive returns more often display smaller exponents, indicating that large positive price variations are more likely than negative ones 
Our results further reveal that changes in the tail exponents are very often simultaneously related to cryptocurrency age and market capitalization or only to age, with only a minority of cryptoassets being affected just by market capitalization or neither of the two quantities  Lastly, we find that the trends in power-law exponents usually point to mixed directions, and that large price variations are likely to become less frequent only in about 28% of the cryptocurrencies as they age and grow in market capitalization 
Since the creation of Bitcoin in 2008 , various different cryptoassets have been developed and are now considered to be at the cutting edge of innovation in finance   These digital financial assets are vastly diverse in design characteristics and intended purposes, ranging from peer-to-peer networks with underlying cash-like digital currencies (e g 
Bitcoin) to general-purpose blockchains transacting in commodity-like digital assets (e g  Ethereum), and even to cryptoassets that intend to replicate the price of conventional assets such as the US dollar or gold (e g  Tether and Tether Gold)   With more than nine thousand cryptoassets as of 2022 , the total market value of cryptocurrencies has grown massively to a staggering $2 trillion peak in 2021  
Despite long-standing debates over the intrinsic value and legality of cryptoassets , or perhaps even precisely due to such controversies, it is undeniable that cryptocurrencies are increasingly attracting the attention of academics, investors, and central banks, around the world   Moreover, these digital assets have been at the forefront of sizable financial gains and losses in recent years , they have been recognized as the main drivers of the brand-new phenomena of cryptoart and NFTs , but also as facilitators of illegal activities, such as money laundering and dark trade  
Financial research dedicated Our results are based on daily price time series of 7111 cryptocurrencies that comprise a significant part of all currently available cryptoassets (see Methods for details)  From these price series, we have estimated their logarithmic returns 2/16 Log-return, r )  The black horizontal arrow represents a given position of the expanding time window (at t = 2004 days) used to sample the return series over the entire history of Bitcoin 
This time window expands in weekly steps (seven time series observations), and for each position, we separate the positive (blue) from the negative (red) price returns  The gray line illustrates observations that will be included in future positions of the expanding time window (t > 2004)  (b) Survival functions or the complementary cumulative distributions of positive (blue) and negative (red) price returns within the expanding time window for t = 2004 days and above the lower bound of the power-law regime estimated from the Clauset-Shalizi-Newman method  
The dashed lines show the adjusted power-law functions, p(r) ∼ r −α , with α = 4 5 for positive returns and α = 3 0 for negative returns  (c) Time series of the power-law exponents α t for the positive (blue) and negative (red) return distributions obtained by expanding the time window from the hundredth observation (t = 100) to the latest available price return of Bitcoin 
The circular markers represent the values for the window position at t = 2004 days and the dashed lines indicate the median of the power-law exponents ( α+ = 4 50 for positive returns and α− = 2 99 for negative returns)  (d) Time series of the p-values related to the power-law hypothesis of positive (blue) and negative (red) price returns for every position of the expanding time window 
The dashed line indicates the threshold (p = 0 1) above which the power-law hypothesis cannot be rejected  For Bitcoin, the power-law hypothesis is never rejected for positive returns (fraction of rejection f r = 0) and rejected in only 4% of the expanding time window positions (fraction of rejection f r = 0 04) 
where x t represents the price of a given cryptocurrency at day t  All return time series in our analysis have at least 200 observations (see Supplementary Figure for the length distribution)  Figure (a) illustrates Bitcoin's series of daily returns  To investigate whether and how returns have changed over the aging and growing processes of all cryptocurrencies, we sample all time series of log-returns using a time window that expands in weekly steps (seven time series observations), starting from the hundredth observation to the latest return observation 
In each step, we separate the positive from the negative return values and estimate their power-law behavior using the Clauset-Shalizi-Newman method   Figure (a) further illustrates this procedure, where the vertical dashed line represents a given position of the time window (t = 2004 days), the blue and red lines indicate positive and negative returns, respectively, and the gray lines show the return observations that will be included in the expanding time window in future steps 
Moreover, Fig  (b) shows the corresponding survival functions (or complementary cumulative distributions) for the positive (blue) and negative (red) returns of Bitcoin within the time window highlighted in Fig  (a)  These survival functions correspond to return values above the lower bound of the power-law regime (r min ) and dashed lines in Fig  (b) show the power-law functions adjusted to data, that is,
with α = 4 5 for the positive returns and α = 3 0 for the negative returns in this particular position of the time window (t = 2004 days)  We have further verified the goodness of the power-law fits using the approach proposed by Clauset et al  (see also Preis et al  )  As detailed in the Methods section, this approach consists in generating several synthetic samples under the power-law hypothesis, adjusting these simulated samples, and estimating the fraction of times the Kolmogorov-Smirnov distance between the adjusted power-law and the synthetic samples is larger than the value calculated from the empirical data 
This fraction defines a p-value and allows us to reject or not the power-law hypothesis of the return distributions under a given confidence level  Following Refs  we consider the more conservative 90% confidence level (instead of the more lenient and commonly used 95% confidence level), rejecting the power-law hypothesis when p-value ≤ 0 1 
For the particular examples in Fig  (b), the p-values are respectively 1 00 and 0 17 for the positive and negative returns, and thus we cannot reject the power-law hypotheses  After sampling the entire price return series, we obtain time series for the power-law exponents (α t ) associated with positive and negative returns as well as the corresponding p-values time series for each step t of the expanding time window 
These time series allow us to reconstruct the aging process of the return distributions over the entire history of each cryptoasset and probe possible time-dependent patterns  Figures ) and 1(d) show the power-law exponents and p-values time series for the case of Bitcoin  The power-law hypothesis is never rejected for positive returns and rarely rejected for negative returns (about 4% of times) 
Moreover, the power-law exponents exhibit large fluctuations at the beginning of the time series and become more stable as Bitcoin matures as a financial asset (a similar tendency as reported by Begušić et al  )  The time evolution of these exponents further shows that the asymmetry between positive and negative returns observed in Fig  ) is not an incidental feature of a particular moment in Bitcoin's history 
Indeed, the power-law exponent for positive returns is almost always larger than the exponent for negative returns, implying that large negative price returns have been more likely to occur than their positive counterparts over nearly the entire history of Bitcoin covered by our data  However, while the difference between positive and negative exponents has approached a constant value, both exponents exhibit an increasing trend, indicating that large price variations are becoming less frequent with the coming-of-age of Bitcoin 
The previous analysis motivates us to ask whether the entire cryptocurrency market behaves similarly to Bitcoin and what other common patterns digital currencies tend to follow  To start answering this question, we have considered the p-values series of all cryptocurrencies to verify if the power-law hypothesis holds in general 
Figure (a) shows the percentage of cryptoassets rejecting the power-law hypothesis in at most a given fraction of the weekly positions of the expanding time window ( f r )  Remarkably, the hypothesis that large price movements (positive or negative) follow a power-law distribution is never rejected over the entire history of about 70% of all digital currencies in our dataset 
This analysis also shows that only ≈2% of cryptocurrencies reject the power-law hypothesis in more than half of the positions of the expanding time window ( f r ≥ 0 5)  For instance, considering a 10% threshold as a criterion ( f r ≤ 0 1), we find that about 85% of cryptocurrencies have return distributions adequately modeled by power laws 
Increasing this threshold to a more lenient 20% threshold ( f r ≤ 0 2), we find large price movements to be power-law distributed for about 91% of cryptocurrencies  These results thus provide strong evidence that cryptoassets, fairly generally, present large price movements quite well described by power-law distributions 
Moreover, this conclusion is robust when starting the expanding window with a greater   Large price movements are power-law distributed over the entire history of most cryptocurrencies with median values typically smaller than those found for traditional assets  (a) Percentage of cryptoassets rejecting the power-law hypothesis for large positive (blue) or negative (red) price returns in at most a given fraction of the weekly positions of the expanding time window ( f r ) used to sample the return series 
Remarkably, 68% of all 7111 digital currencies are compatible with the power-law hypothesis over their entire history, and about 91% of them reject the power-law hypothesis in less than 20% of the positions of the expanding time window ( f r ≤ 0 2)  (b) Probability distributions obtained via kernel density estimation of the median values of the power-law exponents along the history of each digital currency 
The blue curve shows the distribution of the median exponents related to positive returns ( α+ ) and the red curve does the same for negative returns ( α− )  The medians of α+ and α− are indicated by vertical dashed lines  Panels (c) and (d) show the distributions of these median exponents when considering the top 2000 and the top 200 cryptocurrencies by market capitalization, respectively 
We observe that the distributions of α+ and α− tend to shift toward larger values when considering the largest cryptoassets  number of return observations (between 100 and 300 days) and filtering out cryptoassets with missing observations (Supplementary Figures )  Still, it is worth noticing the existence of a few cryptoassets (9 of them) with relatively small market capitalization (ranking below the top 1000) for which the power-law hypothesis is always rejected (Supplementary Table ) 
Having verified that large price movements in the cryptocurrency market are generally well-described by powerlaw distributions, we now focus on the power-law exponents that typically characterize each cryptoasset  To do so, we select all exponent estimates over the entire history of each digital asset for which the power-law hypothesis is not rejected and calculate their median values for both the positive ( α+ ) and negative ( α− ) returns 
The dashed lines in Fig  ) show these median values for Bitcoin where α+ = 4 50 and α− = 2 99  It is worth noticing that the variance of large price movements σ 2 is finite only for α > 3, as the integral σ 2 ∼ ∞ r min r 2 p(r)dr diverges outside this interval  Thus, while the typical variance of large positive returns is finite for Bitcoin, negative returns are at the limit of not having a typical scale and are thus susceptible to much larger variations 
Figure shows the probability distribution for the median power-law exponents of all cryptoassets grouped by large positive and negative returns  We note that the distribution of typical power-law exponents associated with large positive returns is shifted to smaller values when compared with the distribution of exponents related to large negative returns 
The medians of these typical exponents are respectively 2 78 and 3 11 for positive and negative returns  This result suggests that the asymmetry in large price movements we have observed for Bitcoin is an overall feature of the cryptocurrency market  By calculating the difference between the typical exponents related to positive and negative large returns (∆α = α+ − α− ) for each digital currency, we find that about 2/3 of cryptocurrencies have α+ < α− (see Supplementary Figure for the probability distribution of ∆α) 
Thus, unlike Bitcoin, most cryptocurrencies have been more susceptible to large positive price variations than negative ones  While this asymmetry in the return distributions indicates that extremely large price variations tend to be positive, it does not necessarily imply positive price variations are more common for any threshold in the return values 
This happens because the fraction of events in each tail is also related to the lower bound of the power-law regime (r min )  However, we have found the distribution of r min to be similar among the positive and negative returns [Supplementary Figure ]  The distribution of high percentile scores (such as the 90th percentile) is also shifted to larger values for positive returns [Supplementary Figure ] 
Moreover, this asymmetry in high percentile scores related to positive and negative returns is systematic along the evolution of the power-law exponents [Supplementary Figure ]  These results thus indicate that there is indeed more probability mass in the positive tails than in the negative ones, a feature that likely reflects the current expansion of the cryptocurrency market as a whole 
The distributions in Fig  ) also show that large price variations do not have a finite variance for a significant part of cryptoassets, that is, α+ ≤ 3 for 62% of cryptocurrencies and α− ≤ 3 for 44% of cryptocurrencies  A significant part of the cryptocurrency market is thus prone to price variations with no typical scale 
Intriguingly, we further note the existence of a minority group of cryptoassets with α+ ≤ 2 (7%) or α− ≤ 2 (3%)  These cryptocurrencies, whose representative members are Counos X (CCXX, rank 216) with α − = 1 96 and α + = 1 84 and Chainbing (CBG, rank 236) with α + = 1 87, are even more susceptible to extreme price variations as one cannot even define the average value µ for large price returns, as the integral µ ∼ ∞ r min rp(r)dr diverges for α ≤ 2  We have also replicated the previous analysis when considering cryptocurrencies in the top 2000 and top 200 rankings of market capitalization (as of July 2022) 
Figures ) and 2(d) show the probability distribution for the median power-law exponents of these two groups  We observe that these distributions are more localized (particularly for the top 200) than the equivalent distributions for all cryptocurrencies  The fraction of cryptocurrencies with no typical scale for large price returns ( α+ ≤ 3 and α− ≤ 3) is significantly lower in these two groups compared to all cryptocurrencies 
In the top 2000 cryptocurrencies, 51% have α+ ≤ 3 and 26% have α− ≤ 3  These fractions are even smaller among the top 200 cryptocurrencies, with only 44% and 15% not presenting a typical scale for large positive and negative price returns, respectively  We further observe a decrease in the fraction of cryptoassets for which the average value for large price returns is not even finite, as only 2% and 1% of top 2000 cryptoassets have α+ ≤ 2 and α− ≤ 2  This reduction is more impressive among the top 200 cryptocurrencies as only the cryptoasset Fei USD (FEI, rank 78) has α+ = 1 97 and none is characterized by α− ≤ 2  The medians of α+ and α− also increase from 2 78 and 3 11 for all cryptocurrencies to 2
Context 3: Sowing the Seeds of Future Research: Data Sharing, Citation and Reuse in Archaeobotany
Reading: Sowing the Seeds of Future Research: Data Sharing, Citation and Reuse in Archaeobotany
University of Oxford, GB
Lisa is a post-doctoral research fellow at All Souls College, University of Oxford  Her publications include the co-authored volume The Rural Economy of Roman Britain (Britannia Monographs, 2017)  Her research interests are focussed on agricultural practices in the later prehistoric and Roman period and the utilisation of archaeobotanical data to investigate human-plant relationships 
The practices of data sharing, data citation and data reuse are all crucial aspects of the reproducibility of archaeological research  This article builds on the small number of studies reviewing data sharing and citation practices in archaeology, focussing on the data-rich sub-discipline of archaeobotany  Archaeobotany is a sub-discipline built on the time-intensive collection of data on archaeological plant remains, in order to investigate crop choice, crop husbandry, diet, vegetation and a wide range of other past human-plant relationships  Within archaeobotany, the level and form of data sharing is currently unknown  This article first reviews the form of data shared and the method of data sharing in 239 articles across 16 journals which present primary plant macrofossil studies  Second, it assesses data-citation in meta-analysis studies in 107 articles across 20 journals  Third, it assesses data reuse practices in archaeobotany, before exploring how these research practices can be improved to benefit the rigour and reuse of archaeobotanical research 
Keywords: Archaeobotany, Data reuse, Data sharing, Open science
How to Cite: Lodwick, L , 2019  Sowing the Seeds of Future Research: Data Sharing, Citation and Reuse in Archaeobotany  Open Quaternary, 5(1), p 7  DOI: http://doi org/10 5334/oq 62
Accepted on 29 May 2019 Submitted on 25 Mar 2019
Archaeology is a discipline built on the production and analysis of quantitative data pertaining to past human behaviour  As each archaeological deposit is a unique occurrence, ensuring that the data resulting from excavation and analysis are preserved and accessible is crucially important  Currently, there is a general perception of a low level of data sharing and reuse  Such a low level of data availability would prevent the assessment of research findings and the reuse of data in meta-analysis (Kansa & Kansa 2013; Moore & Richards 2015)  As observed across scientific disciplines, there is a major problem in the reproduction of scientific findings, commonly known as the ‘replication crisis’ (Costello et al  2013)  A range of intersecting debates contribute to this, including access to academic findings (open access), open data, access to software and access to methodologies, which can be broadly grouped as open science practices  Without these, the way that scientific findings can be verified and built upon is impaired  Questions of reproducibility have been raised in recent years in archaeology, with considerations of a range of practices which can improve the reproducibility of findings, and a recent call for the application of open science principles to archaeology (Marwick et al  2017)  Discussion has so far focussed on access to grey literature (Evans 2015), data sharing (Atici et al  2013), data citation practices (Marwick & Pilaar Birch 2018) and computational reproducibility (Marwick 2017), with a focus on lithics, zooarchaeological evidence, and archaeological site reports 
Quantitative assessments of current levels of data sharing, data citation and reuse remain limited in archaeology  The focus of evaluation has been on the uptake of large-scale digital archives for the preservation and dissemination of digital data, such as the Archaeology Data Service (ADS), utilised by developer-led and research projects, and recommended for use by many research funders in the UK (Richards 2002; Wright and Richards 2018)  Much less focus has been paid to the data-sharing practices of individuals or small-groups of university-based researchers who may be disseminating their research largely through journal articles  Recent work on the availability of data on lithics assemblages found a low level of data sharing (Marwick & Pilaar Birch 2018) and there are perceptions of low levels of data reuse (Huggett 2018; Kintigh et al  2018)  Within zooarchaeology numerous studies have explored issues of data sharing and reuse (Kansa & Kansa 2013, 2014), and the sub-discipline is seen as one of the most advanced areas of archaeology in regards to open science (Cooper & Green 2016: 273)  Beyond zooarchaeology, however, explicit discussion has remained limited 
This paper assesses data sharing and reuse practices in archaeology through the case study of archaeobotany – a long established sub-discipline within archaeology which has well-established principles of data recording  Archaeobotany is an interesting case study for data sharing in archaeology as it straddles the division of archaeology between scientific and more traditional techniques  Quantitative data on archaeological plant remains are also of interest to a range of other fields, including ecology, environmental studies, biology and earth sciences  The key issues of data sharing and data reuse (Atici et al  2013) have been touched upon in archaeobotany over the past decade within broader discussions on data quality (Van der Veen, Livarda & Hill 2007; Van der Veen, Hill & Livarda 2013)  These earlier studies focussed on the quality and availability of archaeobotanical data from developer-funded excavations in Britain and Cultural Resource Management in North America (Vanderwarker et al  2016: 156)  However, no discussion of data-sharing and reuse in academic archaeobotany occurred  A recent review of digital methods in archaeobotany is the notable exception, with discussions of the challenges and methods of data sharing (Warinner & d’Alpoim Guedes 2014) 
Currently, we have no evidence for the levels of data sharing and reuse within archaeobotany  This article provides the first quantitative assessment of 1) data publication in recent archaeobotanical journal articles 2) data citation in recent archaeobotanical meta-analysis 3) the reuse of archaeobotanical datasets, in order to assess whether practices need to change and how such changes can take place 
2  Data Publication and Re-use Practices in Archaeobotany
2 1  History of data production and publication
Archaeobotanical data falls within the category of observational data in archaeology (Marwick & Pilaar Birch 2018)  Archaeobotanical data is considered as the quantitative assessment of plant macrofossils present within a sample from a discrete archaeological context, which can include species identification, plant part, levels of identification (cf  – confer or “compares to”), and a range of quantification methods including count, minimum number of individuals, levels of abundance and weight (Popper 1988)  Archaeobotanical data is usually entered into a two-way data table organised by sample number  Alongside the counts of individual taxa, other information is also necessary to interpret archaeobotanical data, including sample volume, flot volume, charcoal volume, flot weight, level of preservation, sample number, context number, feature number, feature type and period  Beyond taxonomic identifications, a range of other types of data are increasingly gathered on individual plant macrofossils (morphometric measurements, isotopic values, aDNA) 
Archaeobotanical training places a strong emphasis on recording data on a sample-by-sample basis (Jacomet & Kreuz 1999: 138–139; Jones & Charles 2009; Pearsall 2016: 97–107)  Time-consuming methodologies utilised in the pursuit of accurate sample-level data recording include sub-sampling and splitting samples into size fractions and counting a statistically useful number of items per sample (Van der Veen & Fieller 1982)  The creation of sample-level data means analysis is often undertaken on the basis of individual samples, for instance the assessment of crop-processing stages and weed ecological evidence for crop husbandry practices  The analysis of sample level data also enables archaeobotanical finds to be integrated alongside contextual evidence from archaeological sites  Requirements for the publication of this data are in place in some archaeological guidelines, for instance current Historic England guidelines for archaeological practice in England (Campbell, Moffett & Straker 2011: 8) 
From the earliest archaeobotanical reports, such as Reid’s work at Roman Silchester, the sample from which plant remains were recovered was noted (Lodwick 2017a), but often results were reported as a list of taxa, or long catalogues of detailed botanical descriptions with seed counts, such as Knörzer’s work at Neuss (Knörzer 1970)  Early systematic archaeobotanical reports displayed data within in-text tables, for example Jones’s work at Ashville (Jones 1978) and the two-way data table has been the standard form of reporting archaeobotanical data ever since  Often data tables are presented within book chapters or appendices, but the financial, space and time constraints of book publishing are limiting  Furthermore, there is the perception that specialist data was not necessary for publication (Barker 2001)  Hence, alternative methods of the dissemination of specialist archaeological data were pursued in the later twentieth century 
From the 1980s, archaeobotanical data tables were often consigned to microfiche following a Council for British Archaeology and Department of Environment report (Moore & Richards 2015: 31), with the example of the excavation of Roman Colchester where the contents of all archaeobotanical samples were available on microfiche (Murphy 1992)  An alternative in the 2000s was providing data tables on CD Rom as seen, for instance, in the CD accompanying the study of a Roman farmstead in the Upper Thames Valley (Robinson 2007) or the One Poultry excavations in London (Hill and Rowsome 2011)  Meanwhile, the inception of the Archaeology Data Service, a digital repository for heritage data, in 1996 meant archaeological datasets were increasingly digitally archived, for instance the data from the Channel Tunnel Rail Link Project (Foreman 2018) or a recent large-scale research excavation at Silchester (University of Reading 2018)  In these cases, archaeobotanical data is available to download as a  csv file 
Whilst the data publication strategy of large excavations was shifting, the availability of data from post-excavation assessment reports has remained challenging  So-called ‘grey literature’ results from the initial evaluation stage of developer-funded investigations and accompanying post-excavation assessment often contain a semi-quantitative evaluation of archaeobotanical samples on a scale of abundance  Whilst paper reports were initially deposited with county Historic Environment Records, a process of digitisation focussing on the Roman period has meant many pdfs are now available through the ADS (Allen et al  2018), whilst born-digital reports are now deposited through OASIS (Online AccesS to the Index of archaeological investigationS), as part of the reporting process (Evans 2015), althought the extent to which specialist appendices are included is variable 
These varying ‘publication’ strategies means archaeobotanical data is often available somewhere for recent developer-funded excavations and large-scale developer-funded excavations, even if much of this data is as a printed table or  pdf file (Evans 2015; Evans and Moore 2014)  However, academic journals are typically perceived as the most high-status publication venue for archaeobotanical data, and a crucial publication venue for academics in order to comply with institutional requirements and the norms of career progression  Aside from the problem of access to pay-walled journals by those without institutional subscriptions to all journals, the publication of primary data alongside research articles faces various problems, from the outright lack of inclusion of data, to problematic curation of supplementary data and a lack of peer review of data (Costello et al  2013; Warinner and d’Alpoim Guedes 2014: 155; Whitlock, 2011)  The extent of these problems for archaeobotany is currently unknown  Given the growth in archaeobotanical data production as methodologies are introduced into many new regions and periods over the last decade, it is vital that we know whether the mass of new data being produced is made available and is being reused 
Recent important advances within archaeobotanical data sharing have focussed on the construction of the ARBODAT database, developed by Angela Kreuz at the Kommission für Archäologische Landesforschung in Hessen  The database is used by a range of researchers in Germany, the Czech Republic, France and England (Kreuz & Schäfer 2002)  Data sharing enabled by the use of this database has facilitated research on Neolithic agriculture in Austria, Bulgaria and Germany (Kreuz et al  2005), and Bronze Age agriculture in Europe (Stika and Heiss 2012)  The use of this database makes data integration between specialists easier due to the shared data structure and metadata description, but often the primary archaeobotanical data is not made publicly available 
2 2  Meta-analysis in archaeobotany
Beyond the need to preserve information, a key reason for the formal sharing of archaeobotanical data is in its reuse to facilitate subsequent research  There has been a long-standing concern within archaeobotany with the need to aggregate datasets and identify temporal and spatial patterns



ground_truth: Nuclear liquid-gas transition in lattice QCD.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What type of distribution do the tail distributions of price returns follow?

Read the following text and answer briefly.

Context 2: 98 and 3 35 for the top 2000 and to 3 08 and 3 58 for the top 200 cryptocurrencies 
Conversely, the asymmetry between positive and negative large price returns does not differ much among the three groups, with the condition α+ < α− holding only for a slightly larger fraction of top 2000 (69 1%) and top 200 (70 6%) cryptoassets compared to all cryptocurrencies (66 4%)  Moreover, all these patterns are robust when filtering out time series with sampling issues or when considering only cryptoassets that stay compatible with the power-law hypothesis in more than 90% of the positions of the expanding time window (Supplementary Figures ) 
We also investigate whether the patterns related to the median of the power-law exponents differ among groups of cryptocurrencies with different designs and purposes  To do so, we group digital assets using the 50 most common tags in our dataset (e g  "bnb-chain", "defi", and "collectibles-nfts") and estimate the probability distributions of the median exponents α+ and α− (Supplementary Figures ) 
These results show that design and purpose affect the dynamics of large price variations in the cryptocurrency market as the medians of typical exponents range from 2 4 to 3 7 among the groups  The lowest values occur for cryptocurrencies tagged as "doggone-doggerel" (medians of α+ and α− are 2 38 and 2 83), "memes" (2 41 and 2 87), and "stablecoin" (2 65 and 2 79) 
Digital currencies belonging to the first two tags overlap a lot and have Dogecoin (DOGE, rank 9) and Shiba Inu (SHIB, rank 13) as the most important representatives  Cryptoassets with these tags usually have humorous characteristics (such as an Internet meme) and several have been considered as a form of pump-and-dump scheme , a type of financial fraud in which false statements artificially inflate asset prices so the scheme operators sell their overvalued cryptoassets 
Conversely, cryptoassets tagged as "stablecoin" represent a class of cryptocurrencies designed to have a fixed exchange rate to a reference asset (such as a national currency or precious metal)   While the price of stablecoins tends to stay around the target values, their price series are also marked by sharp variations, which in turn are responsible for their typically small power-law exponents 
This type of cryptoasset has been shown to be prone to failures , such as the recent examples of TerraUSD (UST) and Tron's USDD (USDD) that lost their pegs to the US Dollar producing large variations in their price series  The asymmetry between positive and negative large returns also emerges when grouping the cryptocurrencies using their tags 
All 50 tags have distributions of α+ shifted to smaller values when compared with the distributions of α− , with differences between their medians ranging from −0 74 ("okex-blockdream-ventures-portfolio") to −0 14 ("stablecoin")  Indeed, only four ('stablecoin", "scrypt", "fantom-ecosystem" and "alameda-research-portfolio") out of the fifty groupings have both distributions indistinguishable under a two-sample Kolmogorov-Smirnov test (p-value > 0 05) 
Focusing now on the evolution of the power-law exponents quantified by the time series α t for positive and negative returns, we ask whether these exponents present particular time trends  For Bitcoin [Fig  )], α t seems to increase with time for both positive and negative returns  At the same time, the results of Fig  also suggest that market capitalization affects these power-law exponents 
To verify these possibilities, we assume the power-law exponents (α t ) to be linearly associated with the cryptocurrency's age (y t , measured in years) and the logarithm of market capitalization (log c t )  As detailed in the Methods section, we frame this problem using a hierarchical Bayesian model 
This approach assumes that the linear coefficients associated with the effects of age (A) and market capitalization (C) of each digital currency are drawn from distributions with means µ A and µ C and standard deviations σ A and σ C , which are in turn distributed according to global distributions representing the overall impact of these quantities on the cryptocurrency market 
The Bayesian inference process consists of estimating the posterior probability distributions of the linear coefficients for each cryptocurrency as well as the posterior distributions of µ A , µ C , σ A , and σ C , allowing us to simultaneously probe asset-specific tendencies and overall market characteristics 
Moreover, we restrict this analysis to the 2140 digital currencies having more than 50 observations of market capitalization concomitantly to the time series of the power-law exponents in order to have enough data points for detecting possible trends  When considering the overall market characteristics, we find that the 94% highest density intervals for µ A ([-0 01, 0 06] for positive and [-0 02, 0 03] for negative returns) and µ C ([-0 02, 0 03] for positive and [-0 001, 0 04] for negative returns) include the zero (see Supplementary Figure for their distributions) 
Thus, there is no evidence of a unique overall pattern for the association between the power-law exponents and age or market capitalization followed by a significant part of the cryptocurrency market  Indeed, the 94% highest density intervals for σ A ([0 87, 0 93] for positive and [0 63, 0 70] for negative returns) and σ C ([0 57, 0 61] for positive and [0 49, 0 52] for negative returns) indicate that the cryptocurrency market is highly heterogeneous regarding the evolution of power-law exponents associated with large price variations (see Supplementary Figure for the distributions of σ A and σ C )  Figure illustrates these heterogeneous behaviors by plotting the posterior probability distributions for the linear coefficients associated with the effects of age (A) and market capitalization (C) for the top 20 digital assets, where cryptocurrencies which are significantly affected (that is, the 94% highest density intervals for A or C do not include the zero) by these quantities are highlighted in boldface 
Even this small selection of digital  currencies already presents a myriad of patterns  First, we observe that the power-law exponents of a few top 20 cryptocurrencies are neither correlated with age nor market capitalization  That is the case of Shiba Inu (SHIB, rank 13) and Dai (DAI, rank 11) for both positive and negative returns, UNUS SED LEO (LEO, rank 18) and Polkadot (DOT, rank 12) for the positive returns, and USDCoin (USDC, rank 4) and Solana (SOL, rank 9) for negative returns 
There are also cryptocurrencies with exponents positively or negatively correlated only with market capitalization  Examples include Tether (USDT, rank 3) and Dogecoin (DOGE, rank 10), for which the power-law exponents associated with positive returns increase with market capitalization, and Binance USD (BUSD, rank 6), for which power-law exponents associated with positive and negative returns decrease with market capitalization 
We also observe cryptocurrencies for which age and market capitalization simultaneously affect the power-law exponents  Polygon (MATIC, rank 14) is an example where the power-law exponents associated with positive returns tend to increase with age and decrease with market capitalization  Finally, there are also cryptocurrencies with power-law exponents only associated with age 
That is the case of Bitcoin (BTC, rank 1), Ethereum (ETH, rank 2), and Cardano (ADA, rank 8), for which the power-law exponents related to positive and negative returns increase with age, but also the case of Uniswap (UNI, rank 19), for which the exponents decrease with age  Figure systematically extends the observations made for the top 20 cryptoassets to all 2140 digital currencies for which we have modeled the changes in the power-law exponents as a function of age and market capitalization 
First, we note that only 10% of cryptocurrencies have power-law exponents not significantly affected by age and market capitalization  The vast majority (90%) displays some relationship with these quantities  However, these associations are as varied as the ones we have observed for the top 20 cryptoassets 
About 52% of cryptocurrencies have power-law exponents simultaneously affected by age and market capitalization  In this group, these quantities simultaneously impact the exponents related to positive and negative returns of 34% of cryptoassets, whereas the remainder is affected only in the positive tail (9%) or only in the negative tail (9%) 
Moving back in the hierarchy, we find that the power-law exponents of 32% of cryptocurrencies are affected only by age while a much minor fraction (6%) is affected only by market capitalization  Within the group only affected by age, we observe that the effects are slightly more frequent only on the exponents related to negative returns (12%), compared to cases where effects are restricted only to positive returns (10%) or simultaneously affect both tails (10%) 
Finally, within the minor group only affected by market capitalization, we note that associations more frequently involve only exponents related to negative returns (3%) compared to the other two cases (2% only positive returns and 1% for both positive and negative returns)  Beyond the previous discussion about whether positive or negative returns are simultaneously or individually affected by age and market capitalization, we have also categorized the direction of the trend imposed by these two quantities on the power-law exponents 
Blue rectangles in Fig  represent the fraction of relationships for which increasing age or market capitalization (or both) is associated with a raise in the power-law exponents  About 28% of all cryptocurrencies exhibit this pattern in which large price variations are expected to occur less frequently as they grow and age 
Conversely, the red rectangles in Fig  depict the fraction of relationships for which increasing age or market capitalization (or both) is associated with a reduction in the power-law exponents  This case comprises about 25% of all cryptocurrencies for which large price variations are likely to become more frequent as they grow in market capitalization and age 
Still, the majority of associations represented by green rectangles refer to the case where the effects of age and market capitalization point in different directions (e g  exponents increasing with age while decreasing with market capitalization)  About 36% of cryptocurrencies fit this condition which in turn contributes to consolidating the cumbersome hierarchical structure of patterns displayed by cryptocurrencies regarding the dynamics of large price variations 
This complex picture is not much different when considering only cryptocurrencies in the top 200 market capitalization rank (Supplementary Figure )  However, we do observe an increased prevalence of patterns characterized by exponents that rise with age and market capitalization (37%), suggesting that large price variations are becoming less frequent among the top 200 cryptocurrencies than in the overall market 
)  Each of the previous three levels is further classified regarding whether both positive and negative returns are simultaneously affected or whether the effect involves only positive or only negative returns  Finally, the former levels are classified regarding whether the power-law exponents increase, decrease or have a mixed trend with the predictive variables 
Overall, 36% of the associations are classified as mixed trends (green rectangles), 28% are increasing trends (blue rectangles), and 26% are decreasing trends (red rectangles)  We have studied the distributions of large price variations of a significant part of the digital assets that currently comprise the entirety of the cryptocurrency market 
Unlike previous work, we have estimated these distributions for entire historical price records of each digital currency, and we have identified the patterns under which the return distributions change as cryptoassets age and grow in market capitalization  Similarly to conventional financial assets , our findings show that the return distributions of the vast majority of cryptoassets have tails that are described well by power-law functions along their entire history 
The typical power-law exponents of cryptocurrencies (α ∼ 3) are, however, significantly smaller than those reported for conventional assets (α ∼ 4)   This feature corroborates the widespread belief that cryptoassets are indeed considerably more risky for investments than stocks or other more traditional financial assets 
Indeed, we have found that about half of the cryptocurrencies in our analysis do not have a characteristic scale for price variations, and are thus prone to much higher price variations than those typically observed in stock markets  On the upside, we have also identified an asymmetry in the power-law exponents for positive and negative returns in about 2/3 of all considered cryptocurrencies, such that these exponents are smaller for positive than they are for negative returns 
This means that sizable positive price variations have generally been more likely to occur than equally sizable negative price variations, which in turn may also reflect the recent overall expansion of the cryptocurrency market  Using a hierarchical Bayesian linear model, we have also simultaneously investigated the overall market characteristics and asset-specific tendencies regarding the effects of age and market capitalization on the power-law exponents 
We have found that the cryptocurrency market is highly heterogeneous regarding the trends exhibited by each cryptocurrency; however, only a small fraction of cryptocurrencies (10%) have power-law exponents neither correlated with age nor market capitalization  These associations have been mostly ignored by the current literature and are probably related to the still-early developmental stage of the cryptocurrency market as a whole 
Overall, 36% of cryptocurrencies present trends that do not systematically contribute to increasing or decreasing their power-law exponents as they age and grow in market capitalization  On the other hand, for 26% of cryptocurrencies, aging and growing market capitalization are both associated with a reduction in their power-law exponents, thus contributing to the rise in the frequency of large price variations in their dynamics 
Only about 28% of cryptocurrencies present trends in which the power-law exponents increase with age and market capitalization, favoring thus large price variations to become less likely  These results somehow juxtapose with findings about the increasing informational efficiency of the cryptocurrency market  
In fact, if on the one hand the cryptocurrency market is becoming more informationally efficient, then on the other our findings indicate that there is no clear trend toward decreasing the risks of sizable variations in the prices of most considered cryptoassets  In other words, risk and efficiency thus appear to be moving towards different directions in the cryptocurrency market 
To conclude, we hope that our findings will contribute significantly to the better understanding of the dynamics of large price variations in the cryptocurrency market as a whole, and not just for a small subset of selected digital assets, which is especially relevant due to the diminishing concentration of market capitalization among the top digital currencies, and also because of the considerable impact these new assets may have in our increasingly digital economy 
Our results are based on time series of the daily closing prices (in USD) for all cryptoassets listed on CoinMar-ketCap (coinmarketcap com) as of 25 July 2022 [see Supplementary Figure (a) for a visualization of the increasing number cryptoassets listed on CoinMarketCap since 2013]  These time series were automatically gathered using the cryptoCMD Python package and other information such as the tags associated with each cryptoasset were obtained via the CoinMarketCap API  
In addition, we have also obtained the daily market capitalization time series (in USD) from all cryptoassets which had this information available at the time  Earliest records available from CoinMarketCap date from 29 April 2013 and the latest records used in our analysis correspond to 25 July 2022  Out of 9943 cryptocurrencies, we have restricted our analysis to the 7111 with at least 200 price-return observations 
The median length of these time series is 446 observations [see the distribution of series length in Supplementary Figure   We have estimated the power-law behavior of the return distributions by applying the Clauset-Shalizi-Newman method to the return time series r t   In particular, we have sampled each of these time series using an expanding time window that starts at the hundredth observation and grows in weekly steps (seven data points each step) 
For each position of the expanding time window, we have separated the positive returns from the negative ones and applied the Clauset-Shalizi-Newman method to each set  This approach consists of obtaining the maximum likelihood estimate for the power-law exponent, α = 1 + n/ (∑ n t=1 ln r t /r min ) , where r min is the lower bound of the power-law regime and n is the number of (positive or negative) return observations in the power-law regime for a given position of the expanding time window 
The value r min is estimated from data by minimizing the Kolmogorov-Smirnov statistic between the empirical distribution and the power-law model  The Clauset-Shalizi-Newman method yields an unbiased and consistent estimator , in a sense that as the sample increases indefinitely, the estimated power-law exponent converges in distribution to the actual value 
Moreover, we have used the implementation available on the powerlaw Python package   In addition to obtaining the power-law exponents, we have also verified the adequacy of the power-law hypothesis using the procedure originally proposed by Clauset et al  as adapted by Preis et al    This procedure consists of generating synthetic samples under the power-law hypothesis with the same properties of the empirical data under analysis (that is, same length and parameters α and r min ), adjusting the simulated data with the power-law model via the Clauset-Shalizi-Newman method, and calculating the Kolmogorov-Smirnov statistic (κ syn ) between the distributions obtained from the simulated samples and the adjusted power-law model 
Next, the values of κ syn are compared to the Kolmogorov-Smirnov statistic calculated between empirical data and the power-law model (κ)  Finally, a p-value is defined by calculating the fraction of times for which κ syn > κ  We have used one thousand synthetic samples for each position of the expanding time window and the more conservative 90% confidence level (instead of the more lenient and commonly used 95% confidence level), such that the power-law hypothesis is rejected whenever p-value ≤ 0 1 
We have estimated the effects of age and market capitalization on the power-law exponents associated with positive or negative returns of a given cryptocurrency using the linear model where α t represents the power-law exponent, log c t is the logarithm of the market capitalization, and y t is the age (in years) of the cryptocurrency at t-th observation 
Moreover, K is the intercept of the association, while C and A are linear coefficients quantifying the effects of market capitalization and age, respectively  Finally, N (µ, σ ) stands for the normal distribution with mean µ and standard deviation σ , such that the parameter ε accounts for the unobserved determinants in the dynamics of the power-law exponents 
We have framed this problem using the hierarchical Bayesian approach such that each power-law exponent α t is nested within a cryptocurrency with model parameters considered as random variables normally distributed with parameters that are also random variables  Mathematically, for each cryptocurrency, we have
12/16 where µ K , σ K , µ C , σ C , µ A , and σ A are hyperparameters  These hyperparameters are assumed to be distributed according to distributions that quantify the overall impact of age and market capitalization on the cryptocurrency market as a whole  We have performed this Bayesian regression for exponents related to positive and negative returns separately, and used noninformative prior and hyperprior distributions in order not to bias the posterior estimation  
Specifically, we have considered and ε ∼ U (0, 10 2 ) , where U (a, b) stands for the uniform distribution in the interval [a, b] and Inv−Γ(θ , γ) represents the inverse gamma distribution with shape and scale parameters θ and γ, respectively  For the numerical implementation, we have relied on the PyMC Python package and sampled the posterior distributions via the gradient-based Hamiltonian Monte Carlo no-U-Turn-sampler method 
We have run four parallel chains with 2500 iterations each (1000 burn-in samples) to allow good mixing and estimated the Gelman-Rubin convergence statistic (R-hat) to ensure the convergence of the sampling approach (R-hat was always close to one)  In addition, we have also verified that models describing the power-law exponents as a function of only age (C → 0 in Eq  3) or only market capitalization (A → 0 in Eq  3) yield significantly worse descriptions of our data as quantified by the Widely Applicable Information Criterion (WAIC) and the Pareto Smoothed Importance Sampling Leave-One-Out cross-validation (PSIS-LOO) (see Supplementary Table )  ) is larger than r 90 estimated from negative returns (r − 90 ) 
This fraction is calculated only for weeks in which the power-law hypothesis is not rejected for both tails  The percentage of cryptoassets for which r + 90 > r − 90 is shown in the panels  The first column of panels depicts the results when considering data from all cryptocurrencies, while the second and third columns present the results for the top 2000 and top 200 cryptocurrencies by market capitalization, respectively 
Sampling issues refer to missing data and problems caused by prices of cryptoassets decreasing to zero  We note that these distributions barely change when considering only cryptocurrencies without any sampling issue  Indeed, the distributions in this figure are not significantly distinguishable from their counterparts in Fig  (two-sample Kolmogorov-Smirnov test, p > 0 05) 
)  Each of the previous three levels is further classified regarding whether both positive and negative returns are simultaneously affected or whether the effect involves only positive or only negative returns  Finally, the former levels are classified regarding whether the power-law exponents increase, decrease or have a mixed trend with the predictive variables 
Overall, 35% of the associations are classified as mixed trends (green rectangles), 37% are increasing trends (blue rectangles), and 18% are decreasing trends (red rectangles) 
Context 3:  The palaeobotanist Clement Reid maintained his own database of Quaternary plant records in the late nineteenth century (Reid 1899), which formed the foundation of Godwin’s Quaternary database (Godwin 1975)  Mid-twentieth century studies of prehistoric plant use compiled lists of archaeobotanical materials incorporating full references and the location of the archive (Jessen & Helbaek 1944)  The International Work Group for Palaeoethnobotany was itself founded in 1968 in part with the aim to compile archaeobotanical data, first realised through the publication of Progress in Old World Palaeoethnobotany (Van Zeist, Wasylikowa & Behre 1991), and subsequently through the publication of annual lists of new records of cultivated plants (Kroll 1997) 
To take England as an example, regional reviews produced by state heritage authorities have provided catalogues of archaeobotanical datasets in particular time periods and regions (e g  Murphy 1998)  When one archaeobotanist has undertaken the majority of study within a region, pieces of synthesis within books have provided a relatively comprehensive review, for instance in the Thames Valley, UK (Lambrick & Robinson 2009)  Over the last decade regional synthesis has occurred within several funded reviews which produced catalogues of sites with archaeobotanical data (Lodwick 2014; McKerracher 2018; Parks 2012) and a series of funded projects in France have enabled regional synthesis (Lepetz & Zech-Matterne 2017)  However, many of these reviews are not accompanied by an available underlying database, and draw upon reports which are themselves hard to access 
Through the 1990s and 2000s, a series of databases were constructed in order to collate data from sites in a particular region and facilitate synthetic research  However, these databases have all placed the role of data archiving onto later projects specifically funded to collate data, rather than sourcing datasets at the time of publication  Such a model is unsustainable, and is unlikely to result in all available datasets being compiled  The Archaeobotanical Computer Database (ABCD), published in 1996 in the first issue of Internet Archaeology, contained much of the archaeobotanical data from Britain available at the time of publication, largely at the level of individual samples  The database was compiled between 1989 and 1994 and is still accessible through the accompanying online journal publication (Tomlinson & Hall 1996)  The ABCD made major contributions to recent reviews of the Roman and Medieval periods (Van der Veen, Livarda & Hill 2008; Van der Veen, Hill & Livarda 2013)  However, the database could only be centrally updated, with the online resource remaining a static version, lacking much of the new data produced subsequent to the implementation of PPG16 in 1990  The ADEMNES database, created through a research project undertaken at the Universities of Freiburg and Tübingen, contains data from 533 eastern Mediterranean and Near Eastern sites (Riehl & Kümmel 2005)  Kroll has maintained the Archaeobotanical Literature Database to accompany the Vegetation History and Archaeobotany articles (Kroll 2005) now accessible as a database (Kirleis & Schmültz 2018)  Numerous other databases have collated archaeobotanical studies, including the COMPAG project (Fuller et al  2015), the Cultural Evolution of Neolithic Europe project (Colledge 2016), RADAR in the Netherlands (van Haaster and Brinkkemper 1995), BRAIN Botanical Records of Archaeobotany Italian Network (Mercuri et al  2015) and CZAD – Archaeobotanical database of Czech Republic (CZAD 2019) 
The majority of databases have a restricted regional coverage, whilst research-project driven period-specific databases provide overlapping content  Whilst there are a wide range of archaeobotanical databases available, few contain primary datasets (other than the ABCD) which can be downloaded as  csv files  Data which is most commonly available are bibliographic references per site, with some indications of mode of preservation, quantity of archaeobotanical data, and sometimes taxa present  The databases do not inter-relate to each other, and function primarily as bibliographic sources enabling researchers to find comparative sites or to identify published datasets which need to be re-tabulated prior to meta-analysis  The IWGP website curates a list of resources, but otherwise the resources are often disseminated through the archaeobotany jiscmail list 
Beyond the aim of cataloguing archaeobotanical data within a region and period, meta-analysis is often used in archaeobotany to identify spatial and chronological trends in a range of past human activities, for instance crop choice, crop husbandry practices, plant food consumption, the trade in luxury foods or the use of plants in ritual  Meta-analysis can be undertaken on the basis of simple presence/absence data per site, but in order for such analysis to be rigorous and comparable, sample-level data must be utilised  For instance, sample-level data is required for meta-studies, in order to identify high-quality samples of unmixed crops for weed ecology analysis (Bogaard 2004), to assess the importance of context in the evaluation of wild plant foods (Wallace et al  2019), or to use volumetric measurements as a proxy for scale (Lodwick 2017b)  The reuse of archaeobotanical data also extends to include datasets used as “controls” in commonly used forms of statistical analysis, for instance Jones’s weed data from Amorgos, Greece, which is utilised as a control group in discriminant analysis of crop-processing stage (Jones 1984), and ethnographic observations of crop items in different crop-processing stages (Jones 1990) 
2 3  Open data principles and solutions
Debates over issues of data publication and meta-analysis have been on-going across scientific disciplines over the last decade (Editors 2009), and have been summarised within principles of open science, as recently set out in relation to archaeology (Marwick et al  2017)  Open Data is one of the three core principles for promoting transparency in social science (Miguel et al  2014)  The FAIR principles, developed by representatives from academia, industry, funding agencies, industry and publishers, provide four principles which data sharing should meet for use by both humans and machines – Findability, Accessibility, Interoperability, and Reusability (Wilkinson et al  2016)  A recent report assessing the adoption and impact of FAIR principles across academia in the UK included archaeology as a case study (Allen and Hartland 2018: 46)  It reported how the ADS was often used to archive data, but that “The journal itself provides the “story” about the data, the layer that describes what the data is, how it was collected and what the author thinks it means ” The report also raises the problem that smaller projects may not have the funding to utilise the ADS, meaning that other repositories are utilised  Increasingly, archaeological data is made available through a wide range of data repositories (OSF, Mendeley Data, Zenodo, Open Context), university data repositories (e g  ORA-Data), or social networking sites for academics (Academia edu, ResearchGate)  More widely in archaeology, some have observed that archaeological data is rarely published (Kintigh et al  2014), and recent reviews have reported low levels of data sharing (Huggett 2018; Marwick & Pilaar Birch 2018)  A closely related issue is that of data reuse  Responsible reuse of primary data encourages the sharing of primary data (Atici et al  2013), but levels of data reuse in archaeology are thought to remain low (Huggett 2018)  Principles for responsible data citation in archaeology have recently been developed summarising how datasets should be cited (Marwick & Pilaar Birch 2018) 
In order to assess the current status of data sharing, citation and data re-use in archaeobotany, a review was undertaken of the publication of primary data and the publication of meta-analysis in major archaeological journals over the last ten years, building on recent pilot studies within archaeology (Marwick & Pilaar Birch 2018)  The review of academic journals provided a contrast to recent assessments of archaeobotanical data deriving from developer-funded archaeology (Lodwick 2017c; Van der Veen, Hill & Livarda 2013)  Journal articles have been selected as the focus of this study as the provision of online supplementary materials in the majority of journals and the ability to insert hyperlinks to persistent identifiers (eg a DOI) to link to datasets available elsewhere should not limit the publication of data and references  Much archaeobotanical data is also published elsewhere, especially from projects not based in the university sector, that is commercial or community archaeology in the UK  Archaeobotanical datasets emanating from this research are more commonly published through monographs, county journal articles, and unpublished (or grey literature) reports, but these are beyond the scope of the current review 
All journal articles were included which represent the principle reporting of a new archaeobotanical assemblage  The selected journals fall within three groups  First, what is considered the specialist archaeobotanical journal (Vegetation History and Archaeobotany (VHA))  Second, archaeological science journals (Archaeological and Anthropological Sciences, Environmental Archaeology, The Holocene, Journal of Archaeological Science (JAS), Journal of Archaeological Science: Reports (JASR), Journal of Ethnobiology, Quaternary International, Journal of Wetland Archaeology), which can be considered as specialist sub-disciplinary journals which should be maintaining data-quality  Third, general archaeology journals (Antiquity, Journal of Field Archaeology, Oxford Journal of Archaeology, Journal of Anthropological Archaeology, Journal of World Prehistory)  Finally, the broader cross-disciplinary journals PLoS One and Proceedings of the National Academy of Sciences (PNAS) were included  Published articles from the past ten years (2009–2018) have been analysed in order to assess the availability of plant macrofossil data  This ten-year period brackets the period where most archaeological journals have moved online and adopted supplementary materials 
Data citation in synthetic studies has been assessed in the same range of publications  The extent of data reuse ranges from the analysis of whole sample data to the presence/absence of individual crops  The location of a data citation has been assessed in the same range of publications, with the addition of journals where occasional research incorporating archaeobotanical data is featured (Britannia, Journal of Archaeological Research, Ethnobiology Letters, Medieval Archaeology, Proceedings of the Prehistoric Society, World Archaeology)  The underlying dataset for the analysis is available in Lodwick 2019 
4 1  Primary data sharing
Here, the location of primary archaeobotanical data, that is sample level counts of macroscopic plant remains, was assessed for 239 journal articles across 16 journals (Lodwick 2019 Table 1)  Figure 1 shows the results grouped by journal  Overall, only 56% of articles shared their primary data  In, Antiquity, JAS, JASR, PLOS One, Quaternary International and VHA, the highest proportion of publications did not include their primary data, that is to say that the sample-by-sample counts of plant macrofossils was not available  This level of data is comparable to the findings of other pilot studies in archaeology  Marwick and Pilaar Birch found a data sharing rate of 53% from 48 articles published in Journal of Archaeological Science in Feb – May 2017 (Marwick & Pilaar Birch 2018: 7), and confirm previous assertions that data is often withheld in archaeology (Kansa 2012: 499)  This is better than some disciplines, with a 9% data sharing rate on publication found across high impact journal science publications (n = 500) (Alsheikh-Ali et al  2011) and 13% in biology, chemistry, mathematics and physics (n = 4370) (Womack 2015), yet still indicates that nearly half of articles did not include primary data  Primary archaeobotanical data is more likely to be shared in archaeobotanical and archaeological science journals than general archaeology journals  However, within the primary archaeobotanical journal, VHA, 51% of articles do not include their primary data (Figure 1) 
Chart showing the location of primary archaeobotanical data by journal in primary archaeobotanical data publications 
Where primary data was not shared, the data which was available ranged from summary statistics, typically counts or frequencies, reported either by site, site phase, or feature group  Figure 2 summarises these results by year, showing that there is a gradient within articles not sharing their full ‘raw’ data, from those only provided sample counts on one aspect of the archaeobotanical assemblage, to those only presenting data graphically or within discussion  Beyond full data, the most common form of data shared is either summary counts per site or summary counts per feature or phase  Whilst this data does enable some level of reuse, the results of any sample-level data analysis presented within an article cannot be verified, and the data cannot be reused for crop-processing or weed ecology analysis which requires sample level data  Furthermore, such data would have been collected on a sample-by-sample basis, but this information is lost from the resulting publication 
Chart showing the form of archaeobotanical data shared by year in primary archaeobotanical data publications 
The forms in which data are made available vary across journals  The sharing of primary data within an article remains the most common data sharing form in archaeobotany (Figure 1)  Data tables in text require manual handling to extract data, in journals such as VHA, whilst in other journals in-text tables can be downloaded as 



ground_truth: Power-law functions.

prompt_front: Now, answer the following question based on the text below, only give me the answer and do not output any other words.

Question: What are some reasons for the lack of data sharing in archaeobotany?

Read the following text and answer briefly.

Context 3: csv files  These however would not be citable as a separate dataset  Supplementary datasets are the third most common form of data sharing  Indeed, the use of electronic supplementary material has been advocated recently for by some journals, such as the Journal of Archaeological Science (Torrence, Martinón-Torres & Rehren 2015)  Microsoft Excel spreadsheets are the most common form of supplementary data, followed by  pdfs and then word documents (Figure 1)  Both  xlsx and  docx are proprietary file formats, and not recommended for long term archiving or open science principles  There is no indication of improvement over the last decade in the form of data sharing  In 2018, 50% of articles did not share their primary data, and where the data was shared, it was in proprietary forms ( docx,  xlsx) or those that do not easily facilitate data reuse ( pdf) (Figure 3) 
Chart showing the location of archaeobotanical data from 2009–2018 in primary archaeobotanical data publications 
Just one of the articles included in this review incorporated a dataset archived in a repository (Farahani 2018), in contrast to the substantial growth in data repositories across academic disciplines (Marcial & Hemminger 2010)  Other examples provide the underlying data for monograph publications, such as that of the archaeobotanical data from Gordion, Turkey (Marston 2017a, 2017b), Silchester, UK (Lodwick 2018; University of Reading 2018) and Vaihingen, Germany (Bogaard 2011a; Bogaard, 2011b) 
Several of the journals that have been assessed have research data policies  In the case of Vegetation History and Archaeobotany, sufficient papers have been surveyed to assess the impact of the research data policy on the availability of data  Figure 4 show the proportion of data sharing formats through time just for VHA (note the small sample size)  The introduction of a research data policy in 2016 encouraging data sharing in repositories has not resulted in any datasets being shared in that format  Of the 10 articles published in PLOS One after the introduction of a clear research data policy in 2014, 4 did not contain primary data  However, elsewhere, journals with no research data policy, such as Antiquity, has one of the lower levels of data sharing (Figure 1) 
Chart showing the location of primary archaeobotanical data in Vegetation History and Archaeobotany 
There are various reasons for why a primary dataset may be lacking  The option of providing supplementary datasets has been available in many of the journals here since before the start of the surveyed period (e g  Vegetation History and Archaeobotany in 2004), and so cannot be a reason for the absence of data publication in this journal while it may be a reason in other journals  Reasons suggested for a lack of data sharing within archaeology include technological limitations, and resistance amongst some archaeologists to making their data available due to cautions of exposing data to scrutiny, lost opportunities of analysis before others use it and loss of ‘capital’ of data (Moore & Richards 2015: 34–35)  Furthermore, control over how data tables is presented (taxa ordering, summary data presented) may also contribute to the preferential publishing of data within journal articles  Another factor to consider is the emphasis on the creation of new data through archaeological research (Huvila 2016)  The creation of a new archaeobotanical dataset through primary analysis is a key form of training in archaeobotany, and the perception of the value of the reuse of other previously published archaeobotanical journals may be low, hence not encouraging the sharing of well-documented datasets  Excellent exams of data reuse have resulted in influential studies (Bogaard 2004; Riehl 2008; Wallace et al  2019), and would hopefully encourage further data sharing in the future 
Given that there are numerous examples of meta-analysis which do take place in archaeobotany, it seems likely that the prevalent form of data sharing is through informal data sharing between individual specialists  However, this does not improve access to data in the long term, and is inefficient and time consuming, with large potential for data errors (Kansa & Kansa 2013), and relies on personal networks, which are likely to exclude some researchers  The absence of primary data in many archaeobotanical publications thus inhibits the verification of patterns observed within a dataset, and strongly limits the re-use potential of a dataset 
4 2  Data citation
One of the common arguments for increasing data sharing is an associated increase in the citation of the articles which have data available  Here, the data citation practices of meta-analyses of plant macrofossil data undertaken over the last decade have been reviewed  20 journals were consulted, including a wider range of period-specific journals, and 107 articles were assessed (Lodwick 2019 Table 2)  Data citation was assessed as ‘in text’ or ‘in table’ to refer to when the citation and the bibliographic reference were within the article, as ‘in supplementary data’ when the citation and reference were within the supplementary materials, and as ‘no citation’ when no citation and reference was provided 
21% of articles (n = 22) did not contain any citations to the underlying studies  16% (n = 17) contained citations within supplementary data files  50% of articles (n = 53) contained a citation within a table within the main article, and 14% (n = 15) contained citations within the main text  For the 21% of articles without data citations, the results of these studies could not be reproduced without consulting individual authors  The papers supplying the underlying data also received no credit for producing these datasets  Where articles contain citations within the main article (in text or table), full credit is provided to the underlying studies, a citation link is created through systems such as google scholar, and the study can be easily built upon in the future  Where the citation is provided within supplementary data, the original studies do receive attribution, but are not linked to so easily 
Through time, there is a steady decrease in the proportion of studies without citations to the underlying data, whereby of the 17 meta-analysis articles published in 2018, only one had no data citations  In comparison, in 2009, 3 out of 8 meta-analysis articles contained no data citation (Figure 6)  Overall this is a more positive outlook on the reuse of published data, but the consistent presence of articles lacking data citation indicates that improvements are needed  Reasons for a lack of data citation may include restrictions on word counts imposed by journals, a lack of technical knowledge in making large databases available, or the wish to hold on to a dataset to optimise usage  Considering the type of journal (Figure 5), levels of data citation are worse in general archaeology journals, with sub-disciplinary journals showing slightly better levels of data citation  In particular VHA has a lack of consistency in where data citations are located 
Chart showing the location of data citations in meta-analysis journal articles by journal type 
Chart showing the location of data citations in meta-analysis journal articles from 2009–2018 
4 3  Reuse of archived archaeobotanical datasets
The majority of data citations assessed in the previous section are to articles or book chapters rather than data-sets  The ADS currently hosts 66 data archives which have been tagged as containing plant macro data, deriving mainly from developer-funded excavations but also some research excavations  However, in some of these the plant macro data is contained within a pdf  As, the archiving of archaeobotanical datasets in data repositories is still at an early stage, the reuse of these datasets is assessed here on a case-by-case basis  The archaeobotanical dataset from the Neolithic site of Vaihingen, Germany (Bogaard 2011b) has not been cited on google scholar  Metrics are provided through the ADS, showing this dataset has been downloaded 56 times with 477 individual visits (as of 25/2/19)  The archaeobotanical dataset from Gordion by Marston has no citations on Google Scholar (Marston 2017b), neither does the Giza botanical database (Malleson & Miracle 2018), but these are both very recently archived datasets  In contrast, the Roman Rural Settlement Project dataset, which includes site-level archaeobotanical data, has received greater levels of use, with 12 citations in Google Scholar, over 40,000 file downloads, and over 35,000 visits (Allen et al  2018) and the archaeobotanical computer database (Tomlinson & Hall 1996) has been cited 44 times, and is the major dataset underpinning other highly-cited studies (Van der Veen, Livarda & Hill 2008; Van der Veen, Hill & Livarda 2013)  Whilst there is clearly precedence for the reuse of archaeobotanical databases, current data citation practices within archaeobotany do not yet appear to be formally citing individual datasets, meaning an assessment of the reuse of archived archaeobotanical datasets is challenging 
5  Steps Forward
This review of data sharing, citation, and reuse practices in archaeobotany has found medium levels of data sharing, good levels of data citation, but so far limited levels of reuse of archived data sets  This picture is similar across archaeology, in part attributed to the status of archaeology as a small-science, where data-sharing takes place ad-hoc (Marwick & Pilaar Birch 2018)  Here, recommendations are discussed for improving these data practices within archaeobotany, of applicability more widely in archaeology 
Clearly an important step is improving the sharing of plant macrofossil data  Given the reasonable small size of most archaeobotanical datasets (a  csv file < 1mb), and a lack of ethical conflicts, there seems to be few reasons why the majority of archaeobotanical data couldn’t be shared  In the case of developer-funded derived data, issues of commercial confidentiality could limit the sharing of data  A key stage is establishing why levels of data sharing are not higher  Issues within archaeobotany may include the conflict between having to publish results within excavation monographs, which may take some time to be published, and have limited visibility due to high purchase costs and no digital access, and the need to publish journal articles for career progression within academia  The production of an archaeobotanical dataset is very time-consuming, and interim publication on notable aspects of an assemblage may be considered as a necessary publication strategy  More broadly, one important aspect is issues of equity in access to digital archiving resources (Wright & Richards 2018), such as differential access to funds, training and knowledge  A recent study in Sweden found that we need to know concerns, needs, and wishes of archaeologists in order to improve preservation of archaeological data (Huvila 2016), especially when control of ones data may be linked to perceptions of job security  In order to make improvements in data sharing and reuse across archaeology, we need improved training in data sharing and the reuse of data in higher education (Touchon & McCoy 2016; Cook et al  2018), improved training in data management (Faniel et al  2018), and crucially, the necessary software skills to make the reuse of archived datasets attainable (Kansa & Kansa 2014: 91)  Examples of good practice in archaeobotany are the Vaihingen and Gordion datasets which demonstrate how datasets can be archived in data repositories to accompany a monograph (Bogaard 2011b; Marston 2017b), whilst Farahani (2018) provides an excellent example of a journal article, where the primary data is supplied as a  csv in a cited data repository along with the R script for the analysis 
In tandem with the need to encourage authors to share their data, is the need for journals to create and implement research data policies  Given the existence of research data policies in many of the journals included here, this reflects other findings of the poor enforcement of data policies by journals (Marwick & Pilaar Birch 2018), supporting arguments that journals should not be relied upon to make data accessible, and data should instead by deposited in digital repositries  In order to implement change in data sharing, there is a role to play for learned societies and academic organisation in lobbying funding bodies, prioritising data sharing in research projects  A key step is through journal editorial boards, and the enforcement of any pre-existing research data policies (Nosek et al  2015)  Revi



ground_truth: Technological limitations, resistance to exposing data to scrutiny, and desire to hold onto data for personal use.