Writing data citation guidelines for linguistics: Lessons learned

Poster presented at the RDA 14th Plenary conference, arranged by Research Data Alliance, Helsingfors, 23.10.19 - 25.10.19. https://www.rd-alliance.org/plenaries/rdas-14th-plenary-helsinki-finland. One of the main objectives of the Linguistics Data Interest Group is to develop and adopt common principles and guidelines for data citation and attribution. As a first step, the Austin Principles of Data Citation in Linguistics were published in 2018. Since then, the group has been working on developing data citation guidelines, whose target audiences are academic publishers, resource providers, and researchers who use linguistic data. --- The citation guidelines have been discussed as a work in progress on the 11th and 13th RDA plenaries, and are now more or less ready to be disseminated to the intended audiences for some final feedback. The goal is to finalise and publish the guidelines in late 2019. --- In this poster, we describe the process of developing the linguistic data citation guidelines, a process we believe is transferable to other disciplines. We share our approach to identifying discipline-specific needs while maintaining a connection to data management frameworks and principles that cross fields. We focus on our workflow and organisation, how the relevant communities have been involved in the process, what kind of challenges we have experienced, and how these have been addressed

Writing data citation guidelines for linguistics: Lessons learned

Poster presented at the RDA 14th Plenary conference, arranged by Research Data Alliance, Helsingfors, 23.10.19 - 25.10.19. https://www.rd-alliance.org/plenaries/rdas-14th-plenary-helsinki-finland. One of the main objectives of the Linguistics Data Interest Group is to develop and adopt common principles and guidelines for data citation and attribution. As a first step, the Austin Principles of Data Citation in Linguistics were published in 2018. Since then, the group has been working on developing data citation guidelines, whose target audiences are academic publishers, resource providers, and researchers who use linguistic data. --- The citation guidelines have been discussed as a work in progress on the 11th and 13th RDA plenaries, and are now more or less ready to be disseminated to the intended audiences for some final feedback. The goal is to finalise and publish the guidelines in late 2019. --- In this poster, we describe the process of developing the linguistic data citation guidelines, a process we believe is transferable to other disciplines. We share our approach to identifying discipline-specific needs while maintaining a connection to data management frameworks and principles that cross fields. We focus on our workflow and organisation, how the relevant communities have been involved in the process, what kind of challenges we have experienced, and how these have been addressed

Representational capacity of a set of independent neurons

The capacity with which a system of independent neuron-like units represents a given set of stimuli is studied by calculating the mutual information between the stimuli and the neural responses. Both discrete noiseless and continuous noisy neurons are analyzed. In both cases, the information grows monotonically with the number of neurons considered. Under the assumption that neurons are independent, the mutual information rises linearly from zero, and approaches exponentially its maximum value. We find the dependence of the initial slope on the number of stimuli and on the sparseness of the representation.Comment: 19 pages, 6 figures, Phys. Rev. E, vol 63, 11910 - 11924 (2000

Generalized Fisher information matrix in nonextensive systems with spatial correlation

By using the $q$-Gaussian distribution derived by the maximum entropy method for spatially-correlated $N$-unit nonextensive systems, we have calculated the generalized Fisher information matrix of $g_{\theta_n \theta_m}$ for $(\theta_1, \theta_2, \theta_3) = (\mu_q, \sigma_q^2$, $s$), where $\mu_q$, $\sigma_q^2$ and $s$ denote the mean, variance and degree of spatial correlation, respectively, for a given entropic index $q$. It has been shown from the Cram\'{e}r-Rao theorem that (1) an accuracy of an unbiased estimate of $\mu_q$ is improved (degraded) by a negative (positive) correlation $s$, (2) that of $\sigma_q^2$ is worsen with increasing $s$, and (3) that of $s$ is much improved for $s \simeq -1/(N-1)$ or $s \simeq 1.0$ though it is worst at $s = (N-2)/2(N-1)$. Our calculation provides a clear insight to the long-standing controversy whether the spatial correlation is beneficial or detrimental to decoding in neuronal ensembles. We discuss also a calculation of the $q$-Gaussian distribution, applying the superstatistics to the Langevin model subjected to spatially-correlated inputs.Comment: 18 pages, 3 figures: revised version accepted in Phys. Rev.

Consequences of converting graded to action potentials upon neural information coding and energy efficiency

Information is encoded in neural circuits using both graded and action potentials, converting between them within single neurons and successive processing layers. This conversion is accompanied by information loss and a drop in energy efficiency. We investigate the biophysical causes of this loss of information and efficiency by comparing spiking neuron models, containing stochastic voltage-gated Na+ and K+ channels, with generator potential and graded potential models lacking voltage-gated Na+ channels. We identify three causes of information loss in the generator potential that are the by-product of action potential generation: (1) the voltage-gated Na+ channels necessary for action potential generation increase intrinsic noise and (2) introduce non-linearities, and (3) the finite duration of the action potential creates a ‘footprint’ in the generator potential that obscures incoming signals. These three processes reduce information rates by ~50% in generator potentials, to ~3 times that of spike trains. Both generator potentials and graded potentials consume almost an order of magnitude less energy per second than spike trains. Because of the lower information rates of generator potentials they are substantially less energy efficient than graded potentials. However, both are an order of magnitude more efficient than spike trains due to the higher energy costs and low information content of spikes, emphasizing that there is a two-fold cost of converting analogue to digital; information loss and cost inflation

Investigating mechanisms of state localization in highly ionized dense plasmas

Producción CientíficaWe present experimental observations of Kβ emission from highly charged Mg ions at solid density, driven by intense x rays from a free electron laser. The presence of Kβ emission indicates the n=3 atomic shell is relocalized for high charge states, providing an upper constraint on the depression of the ionization potential. We explore the process of state relocalization in dense plasmas from first principles using finite-temperature density functional theory alongside a wave-function localization metric, and find excellent agreement with experimental results.This work has been supported by the Spanish Ministry of Science and Innovation under Research Grant No. PID2019-108764RB-I0

Investigating Mechanisms of State Localization in Highly-Ionized Dense Plasmas

We present the first experimental observation of K$_{\beta}$ emission from highly charged Mg ions at solid density, driven by intense x-rays from a free electron laser. The presence of K$_{\beta}$ emission indicates the $n=3$ atomic shell is relocalized for high charge states, providing an upper constraint on the depression of the ionization potential. We explore the process of state relocalization in dense plasmas from first principles using finite-temperature density functional theory alongside a wavefunction localization metric, and find excellent agreement with experimental results.Comment: 22 pages, 13 figure