The Microbial Resource Research Infrastructure (MIRRI) stresses the importance of access to microbial data as well as to high quality microorganisms in the execution of sound science and innovative research and development. MIRRI’s mission is to remove fragmentation in resource and service availability and focus on fundamental needs and challenges that face the microbial domain Biological Resource Centres (mBRCs) and the user of microorganisms. MIRRI aims to provide a single access entry point to state-of-the-art microbial biological services and to expert and technical platforms to enable researchers to carry out in-house research on mBRC holdings. This requires improvement in the interoperability between mBRCs and overarching, as well as complementary data offers, and the implementation of quality management including standardised procedures, best practices and appropriate tools to increase the quality of the resources collected and their associated data as well as performed services. The MIRRI consortium currently comprises 16 partner and 28 collaborating parties from 19 countries across Europe and brings together data on microorganism holdings from all these centres. MIRRI partners follow common protocols on data management for mBRC holdings that will enable users to access the microorganisms‘yet unrecognised potential, deliver regulatory compliance and facilitate knowledge and technology transfer. One of the most fundamental problems of managing a collection of microorganisms is keeping pace with the taxonomy and resultant name changes being introduced for species. To overcome such problems MIRRI has produced a data policy and strategy in order to establish an integrated portal for mBRCs.

Statistical noise is usually a main concern in collecting data. Technical malfunction of devices or asynchronous data collection could easily lead to noise appearance. In this paper, we provide some methods for handling noise through the development stages in statistics. While the traditional frequentist approaches could lead to errors in forecasting, methods using Bayesian Statistics “framework” are proposed to deal with noise in data, and issues that need to be improved in these methods are also mentioned.

Memory of food products has been claimed to be more important than the product itself in shaping consumer food choices. For this reason, understanding how to create memorable products for consumers becomes a key aspect to achieve success in the marketplace. The objective of this study was to explore the memorability of cold meat products by investigating which aspects of the eating situations involving this product category are remembered. A total of 152 Brazilians were instructed to remember an occasion in which they were eating cold meat products and to answer six specific questions about the occasion. Results showed that the memorability of cold meat products was strongly related to positive experiences in which consumers enjoyment was due to the characteristics of the products, the people they shared the meal with or the positive emotional state in which they were.

HMMs (HMMs) are well known powerful and flexible statistical methods for modeling one dimensional time series data. They are used when the svector of observation and hidden states are processed. Sometimes, a matrix of data (spatial structure) is dealt with instead of a vector, in this situation there is urgent need to define a new extension of HMM models which can be considered as spatial structure of data.
Discrete HMM (DHMM) is a type of HMMs with discrete observations. This study presents a new extension of the first order DHMM for data with more than one dimension which is a spatial generalization of the first order DHMM. As a matter of fact, this new model will be able to model the matrix of observation and hidden states.