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 is the proposed approach in this research paper?

Read the following text and answer briefly.

Context 1: \section{Introduction}
\label{sec:introduction}

Probabilistic models have proven to be very useful in a lot of applications in signal processing where signal estimation is needed \cite{rabiner1989tutorial,arulampalam2002tutorial,ji2008bayesian}  Some of their advantages are that 1) they force the designer to specify all the assumptions of the model, 2) they provide a clear separation between the model and the algorithm used to solve it, and 3) they usually provide some measure of uncertainty about the estimation 

On the other hand, adaptive filtering is a standard approach in estimation problems when the input is received as a stream of data that is potentially non-stationary  This approach is widely understood and applied to several problems such as echo cancellation \cite{gilloire1992adaptive}, noise cancellation \cite{nelson1991active}, and channel equalization \cite{falconer2002frequency} 

Although these two approaches share some underlying relations, there are very few connections in the literature  The first important attempt in the signal processing community to relate these two fields was the connection between a linear Gaussian state-space model (i e  Kalman filter) and the RLS filter, by Sayed and Kailath \cite{sayed1994state} and then by Haykin \emph{et al } \cite{haykin1997adaptive}  The RLS adaptive filtering algorithm emerges naturally when one defines a particular state-space model (SSM) and then performs exact inference in that model  This approach was later exploited in \cite{van2012kernel} to design a kernel RLS algorithm based on Gaussian processes 

A first attempt to approximate the LMS filter from a probabilistic perspective was presented in \cite{park2014probabilistic}, focusing on a kernel-based implementation  The algorithm of \cite{park2014probabilistic} makes use of a Maximum a Posteriori (MAP) estimate as an approximation for the predictive step  However, this approximation does not preserve the estimate of the uncertainty in each step, therefore degrading the performance of the algorithm 

In this work, we provide a similar connection between state-space models and least-mean-squares (LMS)  Our approach is based on approximating the posterior distribution with an isotropic Gaussian distribution  We show how the computation of this approximated posterior leads to a linear-complexity algorithm, comparable to the standard LMS  Similar approaches have already been developed for a variety of problems such as channel equalization using recurrent RBF neural networks \cite{cid1994recurrent}, or Bayesian forecasting \cite{harrison1999bayesian}  Here, we show the usefulness of this probabilistic approach for adaptive filtering 

The probabilistic perspective we adopt throughout this work presents two main advantages  Firstly, a novel LMS algorithm with adaptable step size emerges naturally with this approach, making it suitable for both stationary and non-stationary environments  The proposed algorithm has less free parameters than previous LMS algorithms with variable step size \cite{kwong1992variable,aboulnasr1997robust,shin2004variable}, and its parameters are easier to be tuned w r t  these algorithms and standard LMS  Secondly, the use of a probabilistic model provides us with an estimate of the error variance, which is useful in many applications 

Experiments with simulated and real data show the advantages of the presented approach with respect to previous works  However, we remark that the main contribution of this paper is that it opens the door to introduce more Bayesian machine learning techniques, such as variational inference and Monte Carlo sampling methods \cite{barber2012bayesian}, to adaptive filtering \\


\section{Probabilistic Model}

Throughout this work, we assume the observation model to be linear-Gaussian with the following distribution,

\begin{equation}
p(y_k|{\bf w}_k) = \mathcal{N}(y_k;{\bf x}_k^T {\bf w}_k , \sigma_n^2),
\label{eq:mess_eq}
\end{equation}
where  $\sigma_n^2$ is the variance of the observation noise, ${\bf x}_k$ is the regression vector and ${\bf w}_k$ is the parameter vector to be sequentially estimated, both $M$-dimensional column vectors 


In a non-stationary scenario, ${\bf w}_k$ follows a dynamic process  In particular, we consider a diffusion process (random-walk model) with variance $\sigma_d^2$ for this parameter vector:


\begin{equation}
p({\bf w}_k|{\bf w}_{k-1})= \mathcal{N}({\bf w}_k;{\bf w}_{k-1}, \sigma_d^2 {\bf I}),
\label{eq:trans_eq}
\end{equation}
where $\bf I$ denotes the identity matrix  In order to initiate the recursion, we assume the following prior distribution on ${\bf w}_k$

\begin{equation}
p({\bf w}_0)= \mathcal{N}({\bf w}_0;0, \sigma_d^2{\bf I}) \nonumber
\end{equation}

\section{Exact inference in this model: Revisiting the RLS filter}

Given the described probabilistic SSM, we would like to infer the posterior probability distribution $p({\bf w}_k|y_{1:k})$ 
Since all involved distributions are Gaussian, one can perform exact inference, leveraging the probability rules in a straightforward manner  The resulting probability distribution is
\begin{equation}
p({\bf w}_k|y_{1:k}) =  \mathcal{N}({\bf w}_k;{\bf\boldsymbol\mu}_{k}, \boldsymbol\Sigma_{k}), \nonumber
\end{equation}
in which the mean vector ${\bf\boldsymbol\mu}_{k}$ is given by
\begin{equation}
{\bf\boldsymbol\mu}_k = {\bf\boldsymbol\mu}_{k-1} + {\bf K}_k (y_k - {\bf x}_k^T {\bf\boldsymbol\mu}_{k-1}){\bf x}_k, \nonumber
\end{equation}
where we have introduced the auxiliary variable
\begin{equation}
{\bf K}_k = \frac{ \left(\boldsymbol\Sigma_{k-1} + \sigma_d^2 {\bf I}\right)}{{\bf x}_k^T  \left(\boldsymbol\Sigma_{k-1} + \sigma_d^2 {\bf I}\right)  {\bf x}_k + \sigma_n^2}, \nonumber
\end{equation}
and the covariance matrix $\boldsymbol\Sigma_k$ is obtained as
\begin{equation}
\boldsymbol\Sigma_k = \left( {\bf I} -  {\bf K}_k{\bf x}_k {\bf x}_k^T \right) ( \boldsymbol\Sigma_{k-1} +\sigma_d^2), \nonumber
\end{equation}
Note that the mode of $p({\bf w}_k|y_{1:k})$, i e  the maximum-a-posteriori estimate (MAP), coincides with the RLS adaptive rule
\begin{equation}
{{\bf w}}_k^{(RLS)} = {{\bf w}}_{k-1}^{(RLS)} + {\bf K}_k (y_k - {\bf x}_k^T {{\bf w}}_{k-1}^{(RLS)}){\bf x}_k  
\label{eq:prob_rls}
\end{equation}
This rule is similar to the one introduced in \cite{haykin1997adaptive} 

Finally, note that the covariance matrix $\boldsymbol\Sigma_k$ is a measure of the uncertainty of the estimate ${\bf w}_k$ conditioned on the observed data $y_{1:k}$  Nevertheless, for many applications a single scalar summarizing the variance of the estimate could prove to be sufficiently useful  In the next section, we show how such a scalar is obtained naturally when $p({\bf w}_k|y_{1:k})$ is approximated with an isotropic Gaussian distribution  We also show that this approximation leads to an LMS-like estimation 
 


\section{Approximating the posterior distribution: LMS filter }

The proposed approach consists in approximating the posterior distribution $p({\bf w}_k|y_{1:k})$, in general a multivariate Gaussian distribution with a full covariance matrix, by an isotropic spherical Gaussian distribution 

\begin{equation}
\label{eq:aprox_post}
\hat{p}({\bf w}_{k}|y_{1:k})=\mathcal{N}({\bf w}_{k};{\bf \hat{\boldsymbol\mu}}_{k}, \hat{\sigma}_{k}^2 {\bf I} ) 
\end{equation}

In order to estimate the mean and covariance of the approximate distribution $\hat{p}({\bf w}_{k}|y_{1:k})$, we propose to select those that minimize the Kullback-Leibler divergence with respect to the original distribution, i e , 

\begin{equation}
\{\hat{\boldsymbol\mu}_k,\hat{\sigma}_k\}=\arg \displaystyle{  \min_{\hat{\boldsymbol\mu}_k,\hat{\sigma}_k}} \{ D_{KL}\left(p({\bf w}_{k}|y_{1:k}))\| \hat{p}({\bf w}_{k}|y_{1:k})\right) \}  \nonumber
\end{equation}

The derivation of the corresponding minimization problem can be found in Appendix A  In particular, the optimal mean and the covariance are found as
\begin{equation}
{\hat{\boldsymbol\mu}}_{k} = {\boldsymbol\mu}_{k};~~~~~~ \hat{\sigma}_{k}^2 = \frac{{\sf Tr}\{ \boldsymbol\Sigma_k\} }{M} 
\label{eq:sigma_hat}
\end{equation}


We now show that by using \eqref{eq:aprox_post} in the recursive predictive and filtering expressions we obtain an LMS-like adaptive rule  First, let us assume that we have an approximate posterior distribution at $k-1$, $\hat{p}({\bf w}_{k-1}|y_{1:k-1}) =  \mathcal{N}({\bf w}_{k-1};\hat{\bf\boldsymbol\mu}_{k-1}, \hat{\sigma}_{k-1}^2 {\bf I} )$  Since all involved distributions are Gaussian, the predictive distribution
is obtained as %
\begin{eqnarray}
\hat{p}({\bf w}_k|y_{1:k-1}) &=& \int p({\bf w}_k|{\bf w}_{k-1}) \hat{p}({\bf w}_{k-1}|y_{1:k-1}) d{\bf w}_{k-1} \nonumber\\
&=& \mathcal{N}({\bf w}_k;{\bf\boldsymbol\mu}_{k|k-1}, \boldsymbol\Sigma_{k|k-1}), 
\label{eq:approx_pred}
\end{eqnarray}
where the mean vector and covariance matrix are given by
\begin{eqnarray}
\hat{\bf\boldsymbol\mu}_{k|k-1} &=& \hat{\bf\boldsymbol\mu}_{k-1} \nonumber \\
\hat{\boldsymbol\Sigma}_{k|k-1} &=& (\hat{\sigma}_{k-1}^2 + \sigma_d^2 ){\bf I}\nonumber 
\end{eqnarray}

From \eqref{eq:approx_pred}, the posterior distribution at time $k$ can be computed using Bayes' Theorem and standard Gaussian manipulations (see for instance \cite[Ch  4]{murphy2012machine})  Then, we approximate the posterior $p({\bf w}_k|y_{1:k})$ with an isotropic Gaussian,
\begin{equation}
\hat{p}({\bf w}_k|y_{1:k}) =  \mathcal{N}({\bf w}_k ; {\hat{\boldsymbol\mu}}_{k}, \hat{\sigma}_k^2 {\bf I} ),\nonumber
\end{equation}
where 
\begin{eqnarray}
{\hat{\boldsymbol\mu}}_{k} &= & {\hat{\boldsymbol\mu}}_{k-1}+ \frac{ (\hat{\sigma}_{k-1}^2+ \sigma_d^2)  }{(\hat{\sigma}_{k-1}^2+ \sigma_d^2)  \|{\bf x}_k\|^2 + \sigma_n^2} (y_k - {\bf x}_k^T {\hat{\boldsymbol\mu}}_{k-1}){\bf x}_k  \nonumber  \\
&=& {\hat{\boldsymbol\mu}}_{k-1}+ \eta_k (y_k - {\bf x}_k^T {\hat{\boldsymbol\mu}}_{k-1}){\bf x}_k   
\label{eq:prob_lms}
\end{eqnarray}
Note that, instead of a gain matrix ${\bf K}_k$ as in Eq ~\eqref{eq:prob_rls}, we now have a scalar gain $\eta_k$ that operates as a variable step size 


Finally, to obtain the posterior variance, which is our measure of uncertainty, we apply \eqref{eq:sigma_hat} and the trick ${\sf Tr}\{{\bf x}_k{\bf x}_k^T\}= {\bf x}_k^T{\bf x}_k= \|{\bf x}_k \|^2$,

\begin{eqnarray}
\hat{\sigma}_k^2 &=& \frac{{\sf Tr}(\boldsymbol\Sigma_k)}{M} \\
&=& \frac{1}{M}{\sf Tr}\left\{ \left( {\bf I} -  \eta_k {\bf x}_k {\bf x}_k^T \right) (\hat{\sigma}_{k-1}^2 +\sigma_d^2)\right\} \\
&=& \left(1 - \frac{\eta_k \|{\bf x}_k\|^2}{M}\right)(\hat{\sigma}_{k-1}^2 +\sigma_d^2) 
\label{eq:sig_k}
\end{eqnarray}
If MAP estimation is performed, we obtain  an adaptable step-size LMS estimation

\begin{equation}
{\bf w}_{k}^{(LMS)} = {\bf w}_{k-1}^{(LMS)} + \eta_k (y_k - {\bf x}_k^T {\bf w}_{k-1}^{(LMS)}){\bf x}_k, 	
\label{eq:lms}
\end{equation}
with
\begin{equation}
\eta_k = \frac{ (\hat{\sigma}_{k-1}^2+ \sigma_d^2)  }{(\hat{\sigma}_{k-1}^2+ \sigma_d^2)  \|{\bf x}_k\|^2 + \sigma_n^2} \nonumber
\end{equation}
At this point, several interesting remarks can be made:

\begin{itemize}

\item The adaptive rule \eqref{eq:lms} has linear complexity since it does not require us to compute the full matrix $\boldsymbol\Sigma_k$ 

\item For a stationary model, we have $\sigma_d^2=0$ in \eqref{eq:prob_lms} and \eqref{eq:sig_k}  In this case, the algorithm remains valid and both the step size and the error variance, $\hat{\sigma}_{k}$, vanish over time $k$  

\item Finally, the proposed adaptable step-size LMS has only two parameters, $\sigma_d^2$ and $\sigma_n^2$, (and only one, $\sigma_n^2$, in stationary scenarios) in contrast to other variable step-size algorithms \cite{kwong1992variable,aboulnasr1997robust,shin2004variable}  More interestingly, both $\sigma_d^2$ and $\sigma_n^2$ have a clear underlying physical meaning, and they can be estimated in many cases  We will comment more about this in the next section  
\end{itemize}



\section{Experiments}
\label{sec:experiments}

We evaluate the performance of the proposed algorithm in both stationary and tracking experiments  In the first experiment, we estimate a fixed vector ${\bf w}^{o}$ of dimension $M=50$  The entries of the vector are independently and uniformly chosen in the range $[-1,1]$  Then, the vector is normalized so that $\|{\bf w}^o\|=1$  Regressors $\boldsymbol{x}_{k}$ are zero-mean Gaussian vectors with identity covariance matrix  The additive noise variance is such that the SNR is $20$ dB  We compare our algorithm with standard RLS and three other LMS-based algorithms: LMS, NLMS \cite{sayed2008adaptive}, VSS-LMS \cite{shin2004variable} \footnote{The used parameters for each algorithm are: for RLS $\lambda=1$, $\epsilon^{-1}=0 01$; for LMS $\mu=0 01$; for NLMS $\mu=0 5$; and for VSS-LMS $\mu_{max}=1$, $\alpha=0 95$, $C=1e-4$ } The probabilistic LMS algorithm in \cite{park2014probabilistic} is not simulated because it is not suitable for stationary environments 

In stationary environments, the proposed algorithm has only one parameter, $\sigma^2_n$  We simulate both the scenario where we have perfectly knowledge of the amount of noise (probLMS1) and the case where the value $\sigma^2_n$ is $100$ times smaller than the actual value (probLMS2)  The Mean-Square Deviation (${\sf MSD} = {\mathbb E} \| {\bf w}_0 - {\bf w}_k \|^2$), averaged out over $50$ independent simulations, is presented in Fig  \ref{fig:msd_statationary} 



\begin{figure}[htb]
\centering
\begin{minipage}[b]{\linewidth}
  \centering
  \centerline{\includegraphics[width=\textwidth]{results_stationary_MSD}}
\end{minipage}
\caption{Performance in terms of MSD of probabilistic LMS with both optimal (probLMS1) and suboptimal (probLMS2) compared to LMS, NLMS, VS-LMS, and RLS }
\label{fig:msd_statationary}
\end{figure}

The performance of probabilistic LMS is close to RLS (obviously at a much lower computational cost) and largely outperforms previous variable step-size LMS algorithms proposed in the literature  Note that, when the model is stationary, i e  $\sigma^2_d=0$ in \eqref{eq:trans_eq},  both the uncertainty $\hat{\sigma}^2_k$, and the adaptive step size $\eta_k$, vanish over time  This implies that the error tends to zero when $k$ goes to infinity  Fig  \ref{fig:msd_statationary} also shows that the proposed approach is not very sensitive to a bad choice of its only parameter, as demonstrated by the good results of probLMS2, which uses a $\sigma^2_n$ that is $100$ times smaller than the optimal value  


\begin{figure}[htb]
\centering
\begin{minipage}[b]{\linewidth}
  \centering
  \centerline{\includegraphics[width=\textwidth]{fig2_final}}
\end{minipage}
\caption{Real part of one coefficient of the measured and estimated channel in experiment two  The shaded area represents two standard deviations from the prediction {(the mean of the posterior distribution)} }
\label{fig_2}
\end{figure}


\begin{table}[ht]
\begin{footnotesize}
\setlength{\tabcolsep}{2pt}
\def1 5mm{1 5mm}
\begin{center}
\begin{tabular}{|l@{\hspace{1 5mm}}|c@{\hspace{1 5mm}}|c@{\hspace{1 5mm}}|c@{\hspace{1 5mm}}|c@{\hspace{1 5mm}}|c@{\hspace{1 5mm}}|c@{\hspace{1 5mm}}|}
\hline
Method &  LMS &  NLMS & LMS-2013 & VSSNLMS & probLMS & RLS \\
\hline
\hline
MSD (dB) &-28 45 &-21 07 &-14 36 &-26 90 &-28 36 &-25 97\\
\hline                                                                     
\end{tabular}
\end{center}
\caption{Steady-state MSD of the different algorithms for the tracking of a real MISO channel }
\label{tab:table_MSD}
\end{footnotesize}

\end{table}
\newpage
In a second experiment, we test the tracking capabilities of the proposed algorithm with {real} data of a wireless MISO channel acquired in a realistic indoor scenario  More details on the setup can be found in \cite{gutierrez2011frequency}  Fig  \ref{fig_2} shows the real part of one of the channels, and the estimate of the proposed algorithm  The shaded area represents the estimated uncertainty for each prediction, i e  $\hat{\mu}_k\pm2\hat{\sigma}_k$  Since the experimental setup does not allow us to obtain the optimal values for the parameters, we fix these parameters to their values that optimize the steady-state mean square deviation (MSD)  \hbox{Table \ref{tab:table_MSD}} shows this steady-state MSD of the estimate of the MISO channel with different methods  As can be seen, the best tracking performance is obtained by standard LMS and the proposed method  





\section{Conclusions and Opened Extensions}
\label{sec:conclusions}

{We have presented a probabilistic interpretation of the least-mean-square filter  The resulting algorithm is an adaptable step-size LMS that performs well both in stationary and tracking scenarios  Moreover, it has fewer free parameters than previous approaches and these parameters have a clear physical meaning  Finally, as stated in the introduction, one of the advantages of having a probabilistic model is that it is easily extensible:}

\begin{itemize}
\item If, instead of using an isotropic Gaussian distribution in the approximation, we used a Gaussian with diagonal covariance matrix, we would obtain a similar algorithm with different step sizes and measures of uncertainty, for each component of ${\bf w}_k$  Although this model can be more descriptive, it needs more parameters to be tuned, and the parallelism with LMS vanishes 
\item Similarly, if we substitute the transition model of \eqref{eq:trans_eq} by an Ornstein-Uhlenbeck process, 

\begin{equation}
p({\bf w}_k|{\bf w}_{k-1})= \mathcal{N}({\bf w}_k;\lambda {\bf w}_{k-1}, \sigma_d^2), \nonumber
\label{eq:trans_eq_lambda}
\end{equation}
a similar algorithm is obtained but with a forgetting factor $\lambda$ multiplying ${\bf w}_{k-1}^{(LMS)}$ in \eqref{eq:lms}  This algorithm may have improved performance under such a kind of autoregresive dynamics of ${\bf w}_{k}$, though, again, the connection with standard LMS becomes dimmer 

\item As in \cite{park2014probabilistic}, the measurement model \eqref{eq:mess_eq} can be changed to obtain similar adaptive algorithms for classification, ordinal regression, and Dirichlet regression for compositional data  

\item A similar approximation technique could be applied to more complex dynamical models, i e  switching dynamical models \cite{barber2010graphical}  The derivation of efficient adaptive algorithms that explicitly take into account a switch in the dynamics of the parameters of interest is a non-trivial and open problem, though the proposed approach could be useful 

\item Finally, like standard LMS, this algorithm can be kernelized for its application in estimation under non-linear scenarios 

\end{itemize}


\begin{appendices}

\section{KL divergence between a general gaussian distribution and an isotropic gaussian}
\label{sec:kl}

 We want to approximate  $p_{{\bf x}_1}(x) = \mathcal{N}({\bf x}; \boldsymbol\mu_1,\boldsymbol\Sigma_1)$ by $p_{{\bf x}_2}({\bf x}) = \mathcal{N}({\bf x}; \boldsymbol\mu_2,\sigma_2^2 {\bf I})$  In order to do so, we have to compute the parameters of $p_{{\bf x}_2}({\bf x})$, $\boldsymbol\mu_2$ and $\sigma_2^2$, that minimize the following Kullback-Leibler divergence,

\begin{eqnarray}
D_{KL}(p_{{\bf x}_1}\| p_{{\bf x}_2}) &=&\int_{-\infty}^{\infty} p_{{\bf x}_1}({\bf x}) \ln{\frac{p_{{\bf x}_1}({\bf x})}{p_{{\bf x}_2}({\bf x})}}d{\bf x} \nonumber  \\
&= &  \frac{1}{2} \{ -M + {\sf Tr}(\sigma_2^{-2} {\bf I}\cdot \boldsymbol\Sigma_1^{-1})  \nonumber \\
  & &  + (\boldsymbol\mu_2 - \boldsymbol\mu_1 )^T \sigma^{-2}_2{\bf I} (\boldsymbol\mu_2 - \boldsymbol\mu_1 )  \nonumber \\
 & &   +  \ln \frac{{\sigma_2^2}^M}{\det\boldsymbol\Sigma_1} \}   
\label{eq:divergence}
\end{eqnarray}
Using symmetry arguments, we obtain 
\begin{equation}
\boldsymbol\mu_2^{*} =\arg \displaystyle{  \min_{\boldsymbol\mu_2}} \{ D_{KL}(p_{{\bf x}_1}\| p_{{\bf x}_2}) \} = \boldsymbol\mu_1 
\end{equation}
Then, \eqref{eq:divergence} gets simplified into 

\begin{eqnarray}
D_{KL}(p_{{\bf x}_1}\| p_{{\bf x}_2}) = \frac{1}{2}\lbrace { -M + {\sf Tr}(\frac{\boldsymbol\Sigma_1}{\sigma_2^{2}}) + \ln \frac{\sigma_2^{2M}}{\det\boldsymbol\Sigma_1}}\rbrace 
\end{eqnarray}
The variance $\sigma_2^2$ is computed in order to minimize this Kullback-Leibler divergence as

\begin{eqnarray}
\sigma_2^{2*} &=& \arg\min_{\sigma_2^2} D_{KL}(P_{x_1}\| P_{x_2}) \nonumber \\
 &=& \arg\min_{\sigma_2^2}\{ \sigma_2^{-2}{\sf Tr}\{\boldsymbol\Sigma_1\} + M\ln \sigma_2^{2} \}  
\end{eqnarray}
Deriving and making it equal zero leads to

\begin{equation}
\frac{\partial}{\partial \sigma_2^2} \left[ \frac{{\sf Tr}\{\boldsymbol\Sigma_1\}}{\sigma_2^{2}} + M \ln \sigma_2^{2} \right] = \left  {\frac{M}{\sigma_2^{2}}-\frac{{\sf Tr}\{\boldsymbol\Sigma_1\}}{(\sigma_2^{2})^2}}\right|_{\sigma_2^{2}=\sigma_2^{2*}}\left  =0 \right   
\nonumber
\end{equation}
Finally, since the divergence has a single extremum in $R_+$,
\begin{equation}
\sigma_2^{2*} = \frac{{\sf Tr}\{\boldsymbol\Sigma_1\}}{M} 
\end{equation}




\end{appendices}

\vfill
\clearpage

\bibliographystyle{IEEEbib}

Context 3: Sir Simon William English  (born 30 December 1961) is a New Zealand former National Party politician who served as the 39th prime minister of New Zealand from 2016 to 2017  He had previously served as the 17th deputy prime minister of New Zealand and minister of finance from 2008 to 2016 under John Key and the Fifth National Government 

A farmer and public servant before entering politics, English was elected to the New Zealand Parliament in  as the National Party's candidate in the Wallace electorate  He was elevated to Cabinet in 1996 and in 1999 was made minister of finance, although he served for less than a year due to his party's loss at the 1999 general election  In October 2001, English replaced Jenny Shipley as the leader of the National Party (and consequently as Leader of the Opposition)  He led the party to its worst defeat at the 2002 general election, and as a consequence, in October 2003 he was replaced as leader by Don Brash 

In November 2006, after Brash's resignation, English became deputy leader under John Key  After National's victory at the 2008 general election, he became deputy prime minister and was also made minister of finance for the second time  Under English's direction New Zealand's economy maintained steady growth during National's three terms of government  He became a list-only MP after stepping down as an electorate MP at the 2014 general election 

John Key resigned as leader of the National Party and prime minister in December 2016  English won the resulting leadership election unopposed and was sworn in as prime minister on 12 December 2016  His tenure was only ten months, and included a three-month election campaign  In the 2017 general election, National won the largest number of seats but fell short of a majority  The parties holding the balance of power declined to support the existing government, and English was subsequently replaced as prime minister by Jacinda Ardern, leader of the Labour Party  English initially continued on as Leader of the Opposition, but resigned as leader of the National Party on 27 February 2018 and left parliament two weeks later 

Early life
English was born on 30 December 1961 at Lumsden Maternity Centre in Lumsden  He is the eleventh of twelve children of Mervyn English and Norah (née O'Brien) English  His parents purchased Rosedale, a mixed sheep and cropping farm in Dipton, Southland from Mervyn's uncle, Vincent English, a bachelor, in 1944  English was born in the maternity unit at Lumsden 

English attended St Thomas's School in Winton, then boarded at St  Patrick's College in Upper Hutt, where he became head boy  He played in the first XV of the school's rugby team  English went on to study commerce at the University of Otago, where he was a resident at Selwyn College, and then completed an honours degree in English literature at Victoria University of Wellington 

After finishing his studies, English returned to Dipton and farmed for a few years  From 1987 to 1989, he worked in Wellington as a policy analyst for the New Zealand Treasury, at a time when the free market policies favoured by Labour's finance minister Roger Douglas (known collectively as "Rogernomics") were being implemented 

English joined the National Party in 1980, while at Victoria University  He served for a period as chairman of the Southland branch of the Young Nationals, and became a member of the Wallace electorate committee  After moving to Wellington, he served for periods on the Island Bay and Miramar electorate committees, respectively 

Fourth National Government (1990–1999)

At the 1990 general election, English stood as the National candidate in Wallace, replacing the retiring Derek Angus, and was elected with a large majority  He would hold this seat, renamed Clutha-Southland in 1996, until 2014  He and three other newly elected National MPs (Tony Ryall, Nick Smith, and Roger Sowry) were soon identified as rising stars in New Zealand politics, and at various points were dubbed the "brat pack", the "gang of four", and the "young Turks"  In his first term in parliament, English chaired a select committee into social services  He was made a parliamentary under-secretary in 1993, serving under the Minister of Health 

First period in cabinet (1996–1999)
In early 1996, English was elevated to cabinet by Prime Minister Jim Bolger, becoming the Minister for Crown Health Enterprises and Associate Minister of Education (to Wyatt Creech)  He was 34 at the time, becoming the cabinet's youngest member  After the 1996 general election, the National Party was forced into a coalition with New Zealand First to retain government  In the resulting cabinet reshuffle, English emerged as Minister of Health  However, as a condition of the coalition agreement, NZ First's Neil Kirton (a first-term MP) was made Associate Minister of Health, effectively becoming English's deputy  This arrangement was described in the press as a "shotgun marriage", and there were frequent differences of opinion between the two ministers  After their relationship became unworkable, Kirton was sacked from the role in August 1997, with the agreement of NZ First leader Winston Peters 

As Minister of Health, English was responsible for continuing the reforms to the public health system that National had begun after the 1990 general election  The reforms were unpopular, and health was perceived as one of the government's weaknesses, with the health portfolio consequently being viewed as a challenge  English believed that the unpopularity of the reforms was in part due to a failure in messaging, and encouraged his National colleagues to avoid bureaucratic and money-focused language (such as references to "balance sheets" and "user charges") and instead talk about the improvements to services the government's reforms would bring  He also rejected the idea that public hospitals could be run as commercial enterprises, a view which some of his colleagues had previously promoted 

By early 1997, as dissatisfaction with Bolger's leadership began to grow, English was being touted as a potential successor, along with Jenny Shipley and Doug Graham  His age (35) was viewed as the main impediment to a successful leadership run  National's leadership troubles were resolved in December 1997, when Bolger resigned and Shipley was elected to the leadership unopposed  English had been a supporter of Bolger as leader, but Shipley reappointed him Minister of Health in her new cabinet 

English was promoted to Minister of Finance in a reshuffle in January 1999, a position which was at the time subordinate to the Treasurer, Bill Birch  After a few months, the pair switched positions as part of Birch's transition to retirement, with English assuming the senior portfolio  In early interviews, he emphasised his wish to be seen as a pragmatist rather than an ideologue, and said that the initiatives of some of his predecessors (Roger Douglas's "Rogernomics" and Ruth Richardson's "Ruthanasia") had focused on "fruitless, theoretical debates" when "people just want to see problems solved" 

Opposition (1999–2008)

After the National Party lost the 1999 election to Helen Clark's Labour Party, English continued on in the shadow cabinet as National's spokesperson for finance  He was elected deputy leader of the party in February 2001, following the resignation of Wyatt Creech, with Gerry Brownlee being his unsuccessful opponent



ground_truth: This research paper proposed an approach based on approximating the posterior distribution with an isotropic Gaussian distribution.

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 2: 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  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  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
Context 3: 

Leader of the Opposition
In October 2001, after months of speculation, Jenny Shipley resigned as leader of the National Party after being told she no longer had the support of the party caucus  English was elected as her replacement unopposed (with Roger Sowry as his deputy), and consequently became Leader of the Opposition  However, he did not openly organise against Shipley, and according to The Southland Times "there was almost an element of 'aw, shucks, I'll do it then' about Mr English's ascension" 

Aged 39 when he was elected, English became the second-youngest leader in the National Party's history, after Jim McLay (who was 38 when elected in 1984)  He also became only the third Southlander to lead a major New Zealand political party, after Joseph Ward and Adam Hamilton  However, English failed to improve the party's performance  In the 2002 election, National suffered its worst electoral defeat ever, gaining barely more than twenty percent of the vote  English described it as "the worst day of my political life"  Both party insiders and the general public were split as to how much to blame him for the loss, but most of the party believed that English would be able to rebuild National's support 

By late 2003, however, National's performance in opinion polls remained poor  The party had briefly increased its popularity in the year following the election, but by October its support had fallen to levels only slightly better than what it achieved in the last ballot  English also appeared in a boxing match for a charity against entertainer Ted Clarke  This did not boost his polling or that of the National party either, with suggestions that it devalued his image as a serious politician  Don Brash, former governor of the Reserve Bank and a relative newcomer to politics, began to build up support to replace English  On 28 October, Brash gained sufficient backing in Caucus to defeat English in a leadership contest 

Shadow cabinet roles and deputy leader
On 2 November 2003, when Brash changed responsibilities for certain MPs, English became National's spokesman for education, ranked at fifth place in the party's parliamentary hierarchy  He remained in parliament after the 2005 election  In his new shadow education portfolio, English performed strongly, and remained a party favourite despite his election defeat as leader in 2002, eventually being returned to the finance portfolio in August 2004 as deputy spokesman (while still retaining responsibility for education) 

In November 2006, Brash resigned as leader  English was considered as a potential replacement leader (running against John Key) or deputy leader (against incumbent Gerry Brownlee) in the ensuing leadership election  However, a contest was avoided when the MPs agreed a Key/English ticket would run unopposed in a display of party unity  English took over the deputy leadership and the finance portfolio in the Key shadow cabinet 

Fifth National Government (2008–2017)

Deputy Prime Minister and Minister of Finance (2008–2016)

At the 2008 election, English was re-elected by his electorate, winning by a margin of about 15,500 votes  He became Deputy Prime Minister of New Zealand and Minister of Finance in the fifth National Government, being sworn into office on 19 November 2008 and continued to serve in those roles until becoming Prime Minister on 12 December 2014  He was also made Minister of Infrastructure in National's first term of government and Minister responsible for Housing New Zealand Corporation and minister responsible for the New Zealand flag consideration process in its third 

He was comfortably re-elected in Clutha-Southland in the 2011 election but opted to run as a party-list candidate in 2014  

The pairing of John Key as leader of the National Party and English as his deputy has been compared to that of Bob Hawke and Paul Keating (in Australia) and Tony Blair and Gordon Brown (in the UK) 

English acceded to the role of Finance Minister in the continuing wake of the financial crisis  In response to New Zealand's rising debt, English made budget deficit-reduction his main priority  His first budget outlined three focuses in New Zealand's financial recovery: "improving the business environment and removing roadblocks to growth; investment in productive infrastructure; and improving the way government works"  One of his first acts was creating the National Infrastructure Unit, charged with formulating a plan for infrastructure projects and investments  He commissioned a government-wide spending review, with an aim to reducing government expenditure—with the exceptions of a two-year stimulus package and long-term increases on infrastructure spending 

In April 2011, the Opposition criticised English for suggesting that New Zealand businesses could use New Zealand's low wages to help it compete with Australia  The National Government campaigned for re-election in 2011 on its economic record  The Government boasted growth for five consecutive quarters up to mid-2010, totalling 1 6% of real GDP 

Strong growth resulted in a surplus of $473 million for the 2015/16 financial year, projected to rise to $8 5 billion by 2020/21  In his 2016 Economic and Fiscal Update address, English stated that reducing debt and tackling the costs of the 2016 Kaikōura earthquake were higher priorities than reducing rates of tax 

Allowances issue
In 2009, the media, including TVNZ and TV3 revealed that English was receiving about NZ$900 a week as part of a living allowance for ministers, to live in his own NZ$1 2 million Wellington home  At the time, English also received $276,200 in his annual salary as Deputy Prime Minister  It was also revealed other ministers with homes in the capital city were also claiming accommodation allowances  On 3 August 2009, Prime Minister John Key started a review of the housing allowances claimed by cabinet ministers  English subsequently paid back $12,000 and only claimed about $24,000 a year in living allowances  The Auditor-General's office said in September 2009 that they were making "preliminary enquiries" into parliamentary housing expenses in response to a letter of complaint from Progressive party leader Jim Anderton  Two days later English stated that he would no longer take up any housing allowance and had paid back all the allowance he had received since the November 2008 election 

Prime Minister (2016–2017)

John Key resigned on 12 December, and endorsed English as his successor in the resulting leadership election  Following the drop-out of both Judith Collins and Jonathan Coleman from the leadership election, English was sworn in as the 39th Prime Minister of New Zealand on 12 December 2016 

English appointed his first cabinet on 18 December  In a reshuffle, he appointed Steven Joyce to succeed him as Finance Minister, while most ministerial portfolios remained the same 

In February 2017, English did not attend Waitangi Day commemorations at the historic treaty grounds, reportedly in response to the Ngāpuhi iwi's decision to stop the Prime Minister from speaking at the marae  Ngāpuhi have protested the Government's negotiation of the Trans Pacific Partnership Agreement (TPPA), which the iwi believe infringes upon Māori sovereignty, and thus does not adhere to the Treaty of Waitangi  English had been invited to attend in an official capacity; his non-attendance was criticised by a Ngāpuhi elder and Opposition leader Andrew Little



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

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: In which electorate was Simon English elected to the New Zealand Parliament?

Read the following text and answer briefly.

Context 3: 

In his first overseas trip as Prime Minister, English travelled to Europe to discuss trade ties, including a prospective New Zealand–European Union free trade agreement  He first travelled to London on 13 January 2017 to meet British Prime Minister Theresa May  Discussing trade relations, English said the two nations were "natural partners" and would "continue to forge ties" after the UK's withdrawal from the EU  He also arranged to meet with London Mayor Sadiq Khan, Belgian Prime Minister Charles Michel and German Chancellor Angela Merkel   In a meeting with Merkel, English received crucial backing from Germany for a trade deal with the EU  On 16 January, English stated that his government would continue to promote TPPA, despite the United States' decision to withdraw from the agreement  He explained that Southeast Asian countries would now be treated as a priority in negotiations—he also asserted that the United States was ceding influence to China by its rejection of the trade pact 

At a press conference at the Beehive on 1 February 2017, English announced that the 2017 general election would be held on 23 September  The Prime Minister later confirmed that his party would approach ACT, United Future and the Māori Party if confidence and supply agreements were required to form a government following the election  In his second cabinet reshuffle on 24 April, English appointed Gerry Brownlee as his new Foreign Affairs Minister; he also promoted Nikki Kaye to the portfolio of Education Minister, and moved Mark Mitchell into the cabinet to become Defence Minister  The reshuffle was perceived as an election preparation 

On 13 February 2017, English welcomed Australian Prime Minister Malcolm Turnbull to Wellington  The two leaders reaffirmed their shared trade agenda, and discussed changes to the Australian citizenship pathway which will affect permanent residents originating from New Zealand 

On 19 June, it was reported that Todd Barclay, who succeeded English as MP for Clutha-Southland, had clandestinely recorded one of his employee's conversations the previous year, and that John Key's leaders' budget was used to pay a confidential settlement after the employee resigned  English admitted that he had been aware of the illegal recording and the settlement, and thus implicated in the scandal 

During the 2017 National campaign launch, English introduced a $379 million social investment package including digital learning academies for high school students, more resources for mathematics, and boosting support for teaching second languages in schools, and maintaining National Standards in the school curriculum  Prime Minister English also sought to defend National's financial management and economic track record and claimed that the opposition Labour Party would raise taxes  Early opinion polling had forecast a poor showing in the election for the Labour Party, but in early August 37-year-old Jacinda Ardern took over as Labour leader and seemingly energised younger voters 

At the 2017 general election, National won the largest share of the party vote (44 4%) and the largest number of seats (56) in the House Representatives  However, National lacked enough seats to govern alone due to two of the party's support partners, the Māori Party and United Future, losing their parliamentary seats  In response, English stated that the party would be entering into talks to form a coalition with New Zealand First  Following talks with the two largest parties, New Zealand First entered a coalition arrangement with the Labour Party  English was succeeded as prime minister by Jacinda Ardern on 26 October 

Opposition (2017–2018)

Leader of the Opposition
English was re-elected as National Party leader on 24 October 2017  At the time of his re-election, English announced his intention to stay on as leader until the next general election  On 13 February 2018, however, he stood down as National Party leader due to personal reasons, and instructed the party to put into motion the processes to elect a new leader  He also retired from Parliament  English's resignation followed weeks of speculation that he would step aside for a new leader  On 27 February, he was succeeded as party leader by Simon Bridges as the result of the leadership election held that day 

Post-premiership 
In 2018, English joined the board of Australian conglomerate, Wesfarmers  English serves in Chairmanships of Mount Cook Alpine Salmon, Impact Lab Ltd and Manawanui Support Ltd  He is also a director of The Instillery, Centre for Independent Studies and The Todd Corporation Limited, and is a member of the Impact Advisory Group of Macquarie Infrastructure and Real Assets 

Political and social views

English is regarded as more socially conservative than his predecessor, John Key  He has stated his opposition to voluntary euthanasia and physician-assisted suicide, same-sex civil unions, and the decriminalisation of prostitution  As Prime Minister he opposed any "liberalisation" of abortion law 

In 2004, English voted against a bill to establish civil unions for both same-sex and opposite-sex couples  In 2005, he voted for the Marriage (Gender Clarification) Amendment Bill, which would have amended the Marriage Act to define marriage as only between a man and a woman  English voted against the Marriage (Definition of Marriage) Amendment Bill, a bill that legalised same-sex marriage in New Zealand  However, in December 2016 he stated, "I'd probably vote differently now on the gay marriage issue  I don't think that gay marriage is a threat to anyone else's marriage" 

In 2009, English voted against the Misuse of Drugs (Medicinal Cannabis) Amendment Bill, a bill aimed at amending the Misuse of Drugs Act so that cannabis could be used for medical purposes 

Personal life 
English met his future wife, Mary Scanlon, at university  She was studying medicine at the time, and became a general practitioner  Both her parents were immigrants, her father being Samoan and her mother Italian, born on the island of Stromboli  They have six children: a daughter and five sons 

English is a practising Roman Catholic, but has stated that he considers his religious beliefs personal and thus separate from politics 

In June 2002, English took part in TV3's Fight For Life, a celebrity boxing fundraiser to raise money for the Yellow Ribbon anti-youth-suicide campaign, influenced by the death of a teenage nephew in 1997  He lost a split decision to former university colleague Ted Clarke 

Honours
In the 2018 Queen's Birthday Honours, English was appointed a Knight Companion of the New Zealand Order of Merit, for services of over 27 years to the State 

See also

List of New Zealand governments
Politics of New Zealand

References

External links

Profile at National Party 
Profile on Parliament nz
Releases and speeches at Beehive govt nz

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1961 births
21st-century New Zealand politicians
Candidates in the 2017 New Zealand general election
Deputy Prime Ministers of New Zealand
Leaders of the Opposition (New Zealand)
Living people
Members of the Cabinet of New Zealand
Members of the New Zealand House of Representatives
New Zealand farmers
New Zealand finance ministers
New Zealand list MPs
New Zealand MPs for South Island electorates
New Zealand National Party MPs
New Zealand National Party leaders
New Zealand Roman Catholics
New Zealand people of Irish descent
People educated at St  Patrick's College, Silverstream
People from Dipton, New Zealand
People from Lumsden, New Zealand
Prime Ministers of New Zealand
University of Otago alumni
Victoria University of Wellington alumni
Knights Companion of the New Zealand Order of Merit
New Zealand politicians awarded knighthoods



ground_truth: The Wallace electorate.