dataesr/scientific-paragraphs-categorization huggingface dataset:
Recent developments in predictive microbial modelling In recent years, predictive microbial modelling has reinforced its position as one of the most promising tools to support microbiological food safety decisions, mainly due to successfully validated models and active software development in this area  [2 ,6,7]. Predictive microbial models can help to understand the microbial behaviour in food systems depending on different environmental factors, being for example a powerful tool to evaluate the microbial exposure within a quantitative microbial risk assessment  [2 ]. Predictive microbiology has the potential to become an even more significant element supporting the microbiological safety of food products in the future. New and more accurate models can be generated through new analytical methods that facilitate the fast characterization of relevant environmental factors and pathogens as well as the acquisition of large datasets.
Recently, Guillier et al. [36]  discussed the importance of another integrative approach to assess a more global overview of food safety concepts by combining sustainability, economy and microbial food safety. An example of economy and risk analysis is the work of Van Wagenberg et al. [37]  on the cost effectiveness of interventions to control Campylobacter on broiler farms. A QMRA should be documented fully and systematically in a transparent manner, and be available to all interested parties, such as risk assessors, risk managers, consumers, industry and the scientific community  [38]. Here, QMRA software tools can be useful. However, the number of software tools intended to QMRA is far less than those aimed at predictive microbial models  [4]. Many of the QMRA models are programmed by using @Risk (Palisade , NY) or R (R: A language and environment for statistical computing; URL: http://www.
R-project. org). While R is free software, @Risk is not, which limits the reuse of these models. Recently, the FDA released version 4. 0 of its webbased risk assessment tool FDA-iRISK 1 (FDA-iRISK 1 version 4. 0. ; URL: https://irisk. foodrisk. org/), that offers users a well-structured template to develop their own QMRA model. The new features include first, separation and quantification of variability and uncertainty through 2D Monte Carlo analysis, second, incorporation of predictive models, third, data importing from FDA databases, fourth, database with shared FDA-iRISK 1 models, and finally, models accounting for multiple hazards and foods among others. A different approach is applied by a new software called FSK-Lab (Food Safety Knowledge Lab; URL: https://foodrisklabs. bfr. bund. de/fsk-lab/) from BfR.
FSK-Lab allows users to create, import, modify, run and export QMRA models in different languages, like R, MATLAB (MATLAB and Statistics Toolbox, The MathWorks, Inc. , Natick, Massachusetts, United States) or KNIME (KNIME; URL: https://www. knime. com/). Besides that, this software allows to combine existing QMRA models facilitating the construction of new or adapted QMRA. Some other resources, like FoodRisk. org (FoodRisk. org; URL: http://foodrisk. org) are also of high relevance. The latter is an open information portal to assist professionals involved in microbial risk analysis and food safety. It contains unique datasets, tutorials, and links to numerous tools and information sources.
Current challenges One of the current challenges in microbiological food safety is the difficulty to exploit and apply the knowledge generated/present in predictive microbiology and QMRA studies and software tools. This represents a severe obstacle for the establishment of timely risk assessment using the most up to date knowledge. Thus, the microbiological food safety community would benefit by having knowledge efficiently shared in a transparent and consistent way in order to facilitate its exploitation and application in microbiological risk assessment and management. This transparent and consistent way can be achieved, for instance, by the development of a user-friendly model repository that stores the knowledge in a standardized format. Thereby users can download models and information about models facilitating their reusability, and also they can easily contribute to develop the model library by uploading models in different programming languages according to the tool they used to build it.
Transparency and consistency Transparency in modelling consists in documenting all the different steps followed within the model generation process, including references for all data used, as well as the assumptions, ranges of applicability, limitations and uncertainties. Consistency principle is a crucial element for allowing model comparison. It means that methods or terminology, once adopted must be applied consistently in future. Also same definitions must be used for similar situations. With the development of new experimental technologies, food microbiologists and risk assessors are now confronted with large datasets that are computationally analyzed for extracting the biological information of interest. Facing the statistical complexity of data analysis and the heterogeneity of available software tools, Cohen-Boulakia et al.
[39 ]  argue that some scientific results will not stand the test of time. Indeed, no one will be able to reproduce results that are dependent of programmes that may not be maintained in the future. Thus, tackling the transparency and consistency of how the results were produced is one of the main scientific challenges. This is essential to enable other researchers to check previous conclusions and build upon them. There are different strategies promoting the adoption of these best practices recommended, for example publishing data into repositories like FigShare or Zenodo  [40] , or as supplement to a publication or publishing data itself in so called 'Data journals'  [41]. Whatever the approach, metadata are key to provide a transparent and consistent description of research results. Harmonized and detailed metadata are the foundation to search for and find datasets, and to be able to reuse knowledge in another context.
Data warehouses typically offer a list of metadata to describe the datasets they host. They may also require a minimum of metadata when submitting datasets. In this sense, a guideline called 'Minimal Information Required in the Annotation of Models' (MIRIAM) has been created to define the minimal information required in the annotation of models within the Systems Biology community  [42]. It is now possible to assign a unique reference (DOI) to a dataset, providing a long-term reference to a digital object. DOI is a service offered by a DOI Registration Agency. Agencies collect metadata, assign DOI names, and offer other services such as reference linking. For example, DataCite  [43]  a non-profit organization not geared to a particular thematic area, can deliver DOIs for datasets as long as a metadata list defined by DataCite is provided.
It supports different standards for metadata, such as Dublin Core (Dublin Core; URL: http://dublincore. org), IDF (International DOI Foundation) metadata kernel, and OECD dataset Metadata. Nowadays the research field of genomics is probably the most advanced area of transparent and consistent knowledge exchange in food microbiology. The large genomic datasets obtained by NGS are usually made publicly available on genome repositories, mainly promoted by the fact that journals in this field usually demand it. Some journals are specifically dedicated to the announcement of new genomic sequences, for example, Genome Announcement. In the same way open software tools used for the exploitation of these datasets are made publicly available in repositories like for example, GitHub or bitbucket  [44].
Researchers involved in the field of predictive microbiology and QMRA modelling are certainly less advanced in transparent and consistent knowledge exchange than, for instance, in the field of genomics or Systems Biology  [42]. However, some examples can be pointed out. Recent studies on the creation of food safety model repositories for predictive microbial models  [45 ]  are likely to provide tailor-made solutions also for this research domain. In November 2016, EFSA launched Knowledge Junction, a curated, open repository that makes use of Zenodo. This repository aims at exchanging evidence and supporting material used in risk assessments, such as QMRA models, datasets, guidance documents, etc. An example of a shared model is a Listeria monocytogenes QMRA for three ready-to-eat foods developed in @Risk software  [46].
In addition, the Interactive online Catalogue on Risk Assessment (ICRA) is also an example of an open repository of QMRA models that allows users to compare and contrast models from the same pathogen and/or commodity (ICRA; URL: http://icra. foodrisk. org/). Although a few repositories for data and models related to microbial responses are available  [47] , in general there is no practice within food microbiology to systematically make all raw data accessible when a study is published. However, dealing with modelling practices associated with these data, the parameters obtained after fitting models to dataset are now generally included in scientific publications. To apply and reuse these data, the related environmental conditions are needed and this is an area where more detailed reporting in scientific publications would be beneficial, as pointed out by predictive microbiology validation studies  [7].
Defining transparent criteria for inclusion of data is important, including the data quality measurement of an obtained kinetic parameter, the number of points of the kinetics and the minimal difference between the inoculated level and maximum level  [48]. The documentation of the fitting procedure is also important. Recent examples for fitting procedures with different tools and fitting approaches can be found in Plaza-Rodriguez et al. [45 ]. The option to provide Supplementary materials in almost every journal in the field should encourage the exchange of knowledge. Some recent studies indicate that more and more data and software scripts will be made publicly available (e. g. [49, 50] ). Transparency and consistency are also a challenge for QMRA. During the last decade, verification and validation have gained a large interest within the scientific community because of the requirement to assess the possible errors affecting the results obtained by software code.
Over the years, many attempts have been made to standardize the terminology of verification and validation, but several distinct definitions are still associated to this concept  [51, 52]. Code and calculation verification are a crucial issue, particularly in the field of risk assessment where computer codes are used to assess the probability of failure of real systems. However, the concept and the practice of code verification are not well developed in the community of QMRA. Among the possible reasons for that are the large variety in scientific background of QMRA modellers, the difficulty to fully understand all details of a QMRA model in the programming language used and the time pressure that is typical associated with performing QMRA in risk assessment agencies, where risk managers need timely assessments to make timely decisions, for example when an outbreak arises.
Software development for knowledge exchange A considerable number of software tools aiming to reuse the generated knowledge in the areas of predictive microbiology and QMRA are available, like ComBase (Com-Base; URL: https://www. combase. cc), PMM-Lab (PMM-Lab; URL: https://foodrisklabs. bfr. bund. de/pmm-lab/), MicroHibro (MicroHibro; URL: http://www. microhibro. com/) and GroPIN (GroPIN; URL: http://www. aua. gr/ psomas/gropin/). Users of specific predictive microbiology software tools have been able to exchange information such as product characteristics, storage condition, kinetic parameters and the related predicted responses. As one example the FSSP-software has used a XML-file format for this purpose since 2004 (Food Spoilage and Safety Predictor (FSSP); URL: http://fssp. food. dtu.
dk/). However, communication mechanisms allowing the exchange of knowledge between different tools have not been established yet. This absence of communication increases the difficulty of users to reach the output of interest and might be a brake of the development of this research field. In the last years, the application of existing XML-based information exchange formats for the microbiological food safety modelling domain has been proposed. Specifications were provided by Plaza-Rodriguez et al. [45 ] , which promoted the consistent adoption of existing standards (NuML/SBML/SED-ML/OMEX) in order to create a common description language for predictive microbial models (Predictive Modelling in Food Markup Language-PMF-ML). PMF-ML has already been used to provide parameterized predictive microbial models to journals  [50]  and has facilitated the direct information exchange between different software tools from the domain of predictive microbiology (ComBase, GroPIN to PMM-Lab; PMM-Lab to R).
Recently, a Food Safety Knowledge Markup Language (FSK-ML; URL: https://foodrisklabs. bfr. bund. de/rakip/) has been suggested (Filter et al. , Workshop 2, 10th ICPMF. Cordoba, Spain, September 2016) in order to create a common description language for QMRA models. The FSK-ML format adapts certain specifications of the PMF-ML format, while maintaining the highest possible synergies between both formats (M Alba Aparicio et al. , COMBINE 2016. Newcastle upon Tyne, UK, September 2016). This will help microbiological food safety models encoded in a software-independent manner (using PMF-ML) to be easily interpreted by FSK-ML import and export functions in the future.
Future perspectives The knowledge continuously generated by the microbiological food safety community should be efficiently shared in order to be able to reuse it for risk assessment and management. The creation of standard-based publicly available knowledge repositories could promote this knowledge sharing. Specifically, the development of harmonized data formats, controlled vocabularies and open source software code libraries, as suggested by Plaza-Rodriguez et al. [45 ] , will facilitate knowledge first, transparency and consistency and second, exchange between software tools in predictive microbial modelling and QMRA. On this basis, we propose to put additional efforts into harmonization of knowledge as the first step to achieve these objectives (Figure  1 ). The markup language FSK-ML has been created to address many of the specific needs of the predictive microbial modelling and QMRA area described in this paper.
). Other more specific formats, such as the Standard Sample Description-SSD2 proposed by EFSA could be used to describe metadata associated to the description of the food product and microbial hazard considered in a model  [54]. It is necessary to create a 'Minimal Information Required in the Annotation of Risk Assessment Models' (MIRARAM) guideline. This guideline would be similar to the MIRIAM guideline  [42]. Even though some metadata would not be defined as mandatory, this would be highly important information, for example, when it comes to the interpretation of model-based predictions. After the models and data have been harmonized and annotated, they should be compiled in a publicly available food safety knowledge repository. This repository would provide free, immediate and permanent access to data and models described in the harmonized data format.
Conclusions Resources and knowledge generated within the microbiological food safety community are increasingly important and numerous. In the last years, advances have been made in new methods for obtaining and processing experimental data, in new model structures and algorithms, together with the creation of databases and software tools to generate and apply mathematical models in the field of predictive microbial modelling and QMRA. New resources facilitating transparent and consistent knowledge exchange would be beneficial to improve microbiological food safety. Synergies between existing resources, like databases and software tools, could be exploited. Therefore, the establishment of harmonized data formats and the development of consistent rules for knowledge annotation would be essential.
Task Description Following the previous lexicon translation settings  (Mikolov et al. , 2013b) , 6K most frequent words from the WMT11 Spanish-English (Sp-En) data 2 are translated into target languages by the online Google Translation (individually for En and Sp). Since Mikolov method requires translation-pairs for training, they used the first 5K most frequent words to learn the "translation matrix", and the remaining 1K words were used as a test set. The proposed method only use parallel sentences for training, so we use the first 5K most frequent words for dimension tuning and the 1K test-pairs for evaluation. To translate a source word, we finds its k nearest target words with Euclidean distance, and then evaluate the translation precision P @k as the fraction of target translations that are within the top-k returned words.
Evaluation Results The evaluation results on test data are shown in Tables  4  and 5. For WMT11, the baseline results are from the reports of corresponding papers. For IWSLT14, the baseline results are from our re-implementations of corresponding methods. The results in bold indicate that they outperform corresponding best baseline results. The numbers in parentheses show how much the best results outperform the best baseline results. Similar as  (Mikolov et al. , 2013b) , we also discard word pairs whose Google translation are out-of-vocabulary.
A global RMSE after co-registration of 17 m is reported by the authors. The authors of  [23]  presented the open-source tool AROSICS targeting a robust and fast co-registration of multi-sensor satellite data. The routine relies on a sub-pixel phase correlation algorithm  [24]  to extract thousands of tie points on regular grids, filtering of outlier matches using Mean Structural Similarity Index  [25]  and a Random Sampling Consensus (RANSAC,  [26] ) to estimate an affine image transformation. The authors present a limited set of experiments indicating an RMSE of 4. 45 m for the co-registration of Landsat-8 to Sentinel-2, 4. 5 m for the co-registration of Landsat-8 to RapidEye-5, and 0. 9 m for the co-registration of TerraSAR-X (StripMap mode) images. Similar figures were also reported by  [27]  for the co-registration of Landsat-8 to Sentinel-2 imagery targeting the harmonization of both datasets at 10 m in the tiling scheme defined by the Landsat Data.
Experimental Setup and Accuracy Assessment A total 178 of Sentinel-2 and Landsat-8 images have been processed to assess the coregis processor in terms of co-registration accuracy and regarding its ability to provide the necessary corrections for displacement measurements. To enable comparison with previous studies, we rely partially on the datasets used in  [27, 28]  where all images are co-registered to one master image (Supplementary Material Table S1/S2). This includes test sites in Argentina, Ukraine, USA and South Africa. A further set of experiments is dedicated to the impact of increasing time lags between the master and slave images on the co-registration accuracy. To this end a dataset of 38 Sentinel-2 images acquired between 5 May 2016 and 21 August 2017 at a test site in the French Alps (tile 31TGK) is used (Supplementary Material Table  S3 ).
57 + -1. 10 m for the tile 34UFU to 14. 10 + -7. 31 m for the tile 20HNH. For the sites displayed in Figure  3  the initial co-registration among Sentinel-2 and Landsat-8 is generally worth than for the Sentinel-2 to Sentinel-2 case with up to 14. 54 + -0. 06 for the tile 34UFU, whereas the same is not true for the two test site displayed in Figure  2. Considering that most Sentinel-2 scenes in these datasets were processed before the yaw bias correction, the initial co-registration errors are consistent with the expected errors (see Section 1). Despite the great variability of the errors before corrections, the RMSE xy after the co-registration generally decreases below 4. 78 m (maximum at the 20HPH test site) with mean RMSE xy of 2. 87+ -0. 61 m, 2. 32 + -0. 67 m, 2. 62 + -1. 29 m, 2. 44 + -0.
Second, coregis relies on the Landsat-8 panchromatic band which provides higher spatial resolution (15 m) than the near-infrared (30 m). Third, the destriping step explicitly addresses along-track stripes which are particularly pronounced for the South Africa test case. Fourth, the dense offset measurements computed with coregis provide, while being computationally expensive (see also Section 3. 4), a complete sampling of the statistical distribution of the offsets used to estimate the image transformation. A second comparison with the AROSICS package  [23]  was performed using the images for the tile 34UFU. AROSICS was used with the recommended default parameters and sub-pixel image correlation was performed on the co-registered images to assess residual offsets. The RMSE xy are compared in Figure  5  showing a mean RMSE xy of 2.
g. , 8 cores at 3 GHz each) depending on the degree of cloud cover and decorrelation within the scene. A significant reduction of the computing time can be achieved by reducing the resolution of the output grids and/or reducing the sub-pixel precision which should enable to adapt coregis also to applications for which processing speed is more important than accuracy. To reduce the computing time for this study all image pairs presented were computed on a dedicated HPC infrastructure. The coregis processor is available as a stand-alone service on the Geohazards Exploitation Platform (GEP) of the European Space Agency geohazards-tep. eo. esa. int. The underlying Python code is also publicly available at github. com/andrestumpf/coregis_public. As shown in Section 3. 3. 2, the purely statistical approach of the correction can introduce biases when most of the scene is affected by ground motion.
The improved accuracy can be attributed mainly to a denser sampling of the displacement field and the explicit use of metadata on the footprints of individual detectors. The incurred additional computation time is addressed through the use of an HPC infrastructure and the coregis package provides functionalities to interact with commonly used HPC job managers. Two use cases demonstrate the application of the processor for measuring surface motion of landslides and co-seimic surface slip. The processing of a combined time-series of Sentinel-2 and Landsat-8 images allowed to document the evolution of the Harmalière landslide and enable to monitor surface displacement rates ranging over two orders of magnitude from 0. 32-13. 30 m day -1. The combination of corrections and multiple-pairwaise image correlation allowed accurate measurements and uncertainty estimates of the surface slip resulting from the 2016 Kaikoura earthquake.
Acknowledgments: This work has been supported by the  European Space Agency  through the project " Geohazards Exploitation Platform " ( ESA/ESRIN 4000115208/15/I-NB ), and the project Alcantara "Automated monitoring of geohazards using Sentinel-2 time series in mountainous regions" ( ESA 15-P26 ), and by the  Council of Europe/Major Hazards Agreement  through the project " Cost-effective ground-based and remote monitoring systems for detecting landslide initiation ". We further thank the  A 2 S (Alsace Aval Sentinelle) research programme of University of Strasbourg  and the  Mésocentre of University of Strasbourg  for additional support and the access to computational resources.