dataesr/scientific-paragraphs-categorization huggingface dataset:
This would actually lead to come back to the variable-master scenario mentioned previously. Altogether, the passage from one master to several seems hence important and relevant from a computational point of view, but it does not modify the analysis about the emergence of free idiosyncrasies. Further analysis of the communicative learning scenario. Interestingly, the communicative model proposed for learning in this study is similar to the communication processes at work in simulations of the emergence of language in societies of interacting agents in a multiagent scenario  [82]. The "interaction games" simulated by e. g. ,  [83, 84]  or  [85] -and our own simulations of the emergence of phonological inventories in COSMO  [58] -are based on various types of interaction situations in which one agent produces a sound and a second agent evaluates the corresponding category and then produces in return an articulatory configuration leading to a sound in the same category.
Conclusion The present work enabled us to reproduce in the COSMO framework experimental data showing coupled perceptual and motor idiosyncrasies relative to the height dimension in French oral vowels. Idiosyncrasies in production were obtained by the implementation of a communicative process in the learning stage. Idiosyncrasies in perception, coupled with idiosyncrasies in production, are best accounted for by a perceptuo-motor theory of perception, though some alternative interpretations have also been discussed. This set of simulations provides a new argument in favor of the existence of a perceptuo-motor coupling in speech perception. Such a coupling might enable the auditory system to focus on the sounds of the environment in an optimal way, while the motor system would exploit personal production strategies to better deal with stimuli differing from the learned environment, that is, in adverse conditions or for dealing with specific kinds of idiosyncrasies (e.
The proposed method is fully in line with these principles, by considering the Open science concept at the very heart of the method. Thus, the method is entirely based on the development and use of Open source components, as well as the free availability of all data produced (i. e. Open data), while ensuring the greatest transparency, regardless of the public that is involved. In addition, the proposed approach is part of a systemic approach to control the data chain, from collection to mapping, which required the implementation of a specific Spatial Data Infrastructure (SDI). The method for collecting the noise data in the environment and the spatial data infrastructure are detailed in Sections 2 and 3. In Section 4, the collected data are described, and, as a preliminary study, the full raw database is analyzed.
General principle The main goal of the approach is to collect noise data and other relevant information, ideally at any location in the environment and at any time. All the data produced by each contributor participates in the feeding of a community database, which should enable a detailed representation of the sound environment in space and over time. To collect data, the contributor uses a specific smartphone application (Section 2. 2). All data are then transmitted to a remote server that is in charge of their post-processing, archiving and sharing (Section 3). In practice, the contributor: 1. starts a measurement with his smartphone; 2. moves along a free path; 3. stops the measurement after a certain time; 4. then, if desired, completes the measurement with some additional information; 5.
Measurement protocol In addition to metrological quality, the realization of the measurement by the contributor is also an essential condition for building a high quality noise database. It is obviously impossible to be 'behind' each contributor to check whether the measurement was carried out in good conditions. However, it is possible to make some recommendations, such as: carrying the smartphone in one hand (not in the pocket), not hiding the microphone, making a measurement without adding an 'artificial' noise, using an external microphone, or calibrating the smartphone. Compliance with these recommendations could be verified, a posteriori, by studying data from other phone sensors as suggested by Rana et al. [28]. However, in the proposed approach, the idea is to limit the collection of data from the smartphone's sensors and to reduce the size of the database that is produced.
This SDI can also be used for other applications requiring environmental data processing. In the context of harmonization, standardization and interoperability, such infrastructure may prove particularly interesting as underlined in Ref. [29]  for applying the European INSPIRE and 2002/49/EC directives  [4, 30]. This central component in the project was developed on the basis of common languages and standards, notably following Open Geospatial Consortium (OGC 1 ) recommendations. The OnoMap SDI is based on five Open source applications (Fig. 2 ), deployed on a remote server to which data are transferred from the NoiseCapture application  [32] : • A spatial relational database, using the couple 'PostGreSQL/PostGIS' and allowing to store, organize and process the data; • A map server 'GeoServer', used to display data (stored in the data- base) through standardized streams such as WMS (Web Map Service) and WFS (Web Feature Service); • A web-mapping library, based on the 'LeafLet' component, allowing the creation of interactive maps that can be consulted on a web page in order to view and query noise data; • Processing tools, based on the 'H2GIS' component (geospatial functions library) and the 'OrbisGIS' software  [33] , used to describe and execute online processing on the data, in compliance with the Web Processing Service (WPS) standard.
Contributor behavior 4. 3. 1. App (un-)installs Regarding the evolution of the number of users/contributors, Fig. 7  shows the daily installs and un-installs of the Android application on a device (data from Google Play Console), compared to the new contributors to the database each day. At first, the first two peaks (at the end of August and at the beginning of September) are due to the launch of the application and the enthusiasm it generated through the information relayed by the media. Over the considering period, the total number of installs (acquisition of new users) and un-installs are 50627 and 37591 respectively, leading to a number of 'active' installs of 13036. On these remaining installs, about 9663 installations have been really active in the last 30 days (i. e. 19% of the first installs).
In details, Fig. 8 (a) shows the installer retention rate by month. As example, an installer-to-30 days retention rate of 62% (August 2017) means that 62% of users are still using the application 30 days after the first installation. One can observe that, even if the number of first installs is decreasing in the last months (as already observed at Fig. 7 ), the installer-to-30 days retention rate tends to stabilize around 32% (note that more data are needed to establish long-term statistics). Comparing the number of new contributors to the number of installs, it shows that not all users contribute to the data collection for the database: the ratio contributors/users is around 47% over the whole considered period. Fig. 8 (b) shows that the behavior that is observed for the Noise-Capture App, is quite similar to those observed for most Android Apps.
Using collected data for environmental noise assessment Once the database has been qualified, the question turns to the best possible use of the data to assess and improve the sound environment. At this stage, several applications are possible. The most direct application is the production of noise maps, including all the sound sources. Since the assessment of noise levels is based on measurements, we can therefore expect a more realistic representation of the sound environment, compared to approaches based solely on modelling. The other advantage of this approach is the possibility of having a temporal description of sound levels, instead of a long-term assessment This will be all the more relevant as the same geographical area will have given rise to measurements at different times of the day, for different weekdays and at different times of the year.
[40]. The assessment of the quality of the sound environment cannot be limited to the knowledge of sound level indicators alone. Very recent models now make it possible to assess the quality of the environment on the basis of both physical indicators and knowledge of the presence of sound sources  [14, 15]. The possibility of using tags and source identification processing, as discussed above, would therefore also make it possible to produce sound maps closer to the perception  [41]. As mentioned above, a large number of the data collected are described with the 'Indoor' tag, which potentially opens the way for the database to be used for large-scale indoor assessment purposes. However, one of the difficulties lies in the ability to properly locate a measurement in a particular building (requires a different location technology than GPS) or to assign a measurement to a building (by crossing geolocated database).
Once this problem of location has been eliminated, it would therefore be possible to obtain a noise level estimation inside closed areas, such as public places (restaurants, shopping centres, gaming rooms, etc. ). As discussed above, knowledge of the speed associated with each measurement point makes it possible to exclude measurements that would not correspond to a pedestrian. On the other hand, the data discarded would therefore a priori be associated with mechanical modes  of transport (bicycle, motorcycle, bus, tramway, train, etc. ). Cross-referencing the GPS position of these measurement points with other geolocated database would make it possible to identify the mode of transport used. Therefore, it is potentially possible to assess noise levels within certain modes of transport, which would therefore complement the assessment of the outdoor sound environment.
However, the quality of this evaluation depends very much on the quality of the data collected, and particularly on the metrological quality of smartphones, both from the acoustic and geolocation points of view, but also on the measurement protocol. Considering the metrological aspects, the permanent and rapid evolution of smartphone capabilities should contribute to the continuous improvement of the database. Recent works have shown, for example, that a smartphone application on iOS, with an external microphone could achieve compliance with most of requirements for Class 2 sonometers  [42]. In addition, the implementation of specific protocols for acoustic calibration of smartphones could also be considered. With regard to the measurement protocol aspects, it seems appropriate to focus primarily on contributors with some technical expertise in the field of environmental noise measurement.
In particular, the organization of one-off events (NoiseCapture Parties) supervised by specialists ('Ambassadors') should contribute to produce a more relevant database. As example, in France, contacts have already been made with technical services of local authorities and state services to set up a community of experts. The statistical analysis of the first contributions to the database already provides many lessons. Even if a huge number of installations of the application is observed, many of them are very short in duration (i. e. some people install the application, try it and uninstall it immediately). As for many smartphone applications, the retention rate after several weeks decreases gradually, but in the case of NoiseCapture, the behavior is similar to that of the best Android applications, which is very positive.
• Voltages from temperature and humidity sensors with a maximum temperature uncertainty over the measuring range [-20 C; 40 • C] is 0. 2 • C. The acquisition unit requires a 24 V DC power supply. It has been connected to an autonomous circuit. The 24 V DC came from batteries regularly replaced and connected to the acquisition unit via a converter, ensuring stable DC voltage during the initial connection of the installation. In a second stage, photovoltaic panels of about 0. 5 m 2 were installed, in order to provide a maximum power of about 100 W. The acquisition unit has an embedded operating system. It has a 2 GB memory and offers the possibility to connect an SD card. A Labview-coded program was developed in laboratory to record data from all the acquisition cards simultaneously at a desired frequency.
The data is recorded on an SD card connected to the control unit. The acquisition of the signals of the Bragg grating optical fiber sensors (FBG) during the first months is carried out punctually with a portable device (HBM FS42 Portable BraggMETER). The resistivity measurement is carried out punctually with a resistivimeter ABEM terrameter LS, which is used as standard by geophysicists and is suitable for civil engineering applications  [11, 17]. The whole installation (batteries, electronics, central unit, etc. ) allowing data acquisition has been integrated into waterproof boxes to protect it from external aggressions (wind, rain, tide, etc. ). The boxes are positioned at the end of the platform as shown in Figures  3  and 4.
The simultaneous start of the acquisition with concreting made it possible to validate the operation of the measuring chain and the identification of the malfunction of failed sensors. Analysis of the data at early age showed that strain and resistivity measurements could be directly exploited within the framework of manufacturing process monitoring. However, to be fully exploitable, these measurements must be combined with temperature measurements. Moisture measurements and material testing also provide interesting additional information for analysis. Strength measurements combined with the determination of activation energy, and the monitoring of resistivity, allow comparing the development of mechanical and physical concrete properties and showing their correlation. The study presented here proposes a multi-physical database of in situ measurements or material tests collected during one year.
The initial state of the structure and the material are therefore perfectly known. The data presented in this paper is meant to be updated within five to 10 years once the degradation process has begun. The knowledge of the complete measurement history will promote a better understanding of degradation processes. Additionally, the study shows that a measurement chain dedicated to the SHM could be a tool for validating the quality of the construction of a reinforced concrete structure. The measuring chain allows early detection of structural disorders, thus limiting the risk of costly repairs during the operating phase. The data collected on site and the results of the material tests presented in this article are available in the Supplementary Materials. The aim is to propose a database to analyze the link between measurements carried out in laboratory with data recorded during the construction of a real structure.
Acknowledgments: The authors would like to thank all the partners of iMAREO2 project: Keops Automation ( D. Follut ,  D. Olivier ),  Université de Nantes (M. Roche), Nantes-Saint Nazaire Port (P. Lijour, M. Labegorre) and Bouygues Construction  (the teams of Bouygues Travaux Publics Régions France). Special thanks are due to  Thierry Giorgetti  (acquisition unit installation) and  Antoine Bassil  (sensors installation) for their help in this project. The authors would like to thank the  Pays de la Loire region  for its financial support.
NormFinder analysis. We next analyzed our data using NormFinder which uses a model-based approach which calculates the stability of reference genes based on two parameters-the intergroup variation and the intragroup variation. The stability score denoted by the S value is a weighted measure of these two parameters. The most stable reference gene has the least S value. In the cerebellum, NormFinder identified Ppia, Mrpl10 and Sdha as the top three stable genes. In the spinal cord, the algorithm identified Mrpl10, Ppia and Hsp60 as the top three stable reference genes. GeNorm analysis. GeNorm calculates stability based on pairwise variation. The rationale is that if two genes vary similarly across all samples, then they are the most stable reference genes for that dataset. All other genes are ranked on their similarity to the expression of the top two genes.
Suitability of validation methods for longitudinal studies We next analyzed the suitability of these methods in a longitudinal setting highlighting the assumptions of each method and the factors that influence the stability scores. CV analysis. As mentioned, the CV analysis adopts a direct approach by calculating the variance of a gene across all samples taken together. However, this method does not consider the variation across different time points or groups. For example, Actb is ranked 2 nd in the cerebellum (Table  1 ) as it exhibits low overall variation but the profile of the gene (Fig  2A ) tells us that it does vary across time points. This is a major concern in a longitudinal study as this method determines variation in just one dimension whereas the dataset exists in two dimensions.
This is also evident from the raw expression profiles where we see that the raw expression profiles in the cerebellum are heterogeneous (Fig  2 ). In the spinal cord, however, all the reference genes seem to have a similar profile except for Actb and Rps26 (Fig  3 ). , Interestingly, the highest correlation in the cerebellum dataset was observed in Gapdh/Tbp with a Pearson's r score of 0. 937. This gene pair is ranked first in the GeNorm analysis (Table  1 ) but have the highest overall variation in the group (CV = 59. 1% and 57. 2%). In the spinal cord, however, the top two genes according to GeNorm are Mrpl10/Rpl13a while the highest correlation is observed in Sdha/Pgk1 with a Pearson's r score of 0. 97. Intriguingly, the genes that are classed below-Pgk1, Hsp60 and Tbp have CV values of 57.
8%, 49. 4% and 58. 6%, which are among the highest in the spinal cord group. This is because of their   1 ). These results suggest that GeNorm tends to favor highly correlated genes in a heterogeneously correlated set of genes (cerebellum dataset). In a less heterogenous dataset (spinal cord) the ranking does not concur with the overall stability of the genes across samples assessed by visual representation (Fig  3 ) and CV analysis (Table  1 ). These results taken together show that GeNorm's ranking can be influenced by the extent of correlation in the dataset. This exclusion of possibly stable genes by GeNorm because of negative or less correlation was indeed foreseen by Anderson and colleagues when they proposed the NormFinder model. We find that to be true in our analysis. Pairwise ΔCt Analysis.
Primer design All primers used in the study were designed using the Primer 3 plus software (https:// primer3plus. com/cgi-bin/dev/primer3plus. cgi). Splice variants and the protein coding sequence of the genes were identified using the Ensembl database (www. ensembl. org). Constitutively expressed exons among all splice variants were then identified using the ExonMine database (www. imm. fm. ul. pt/exonmine/). Primer sequences that generated amplicons spanning two constitutively expressed exons were then designed using the Primer plus software. For detailed information on Primer sequences refer to Table  3.
Acknowledgments The authors did not receive any specific funding for this study. The authors would like to thank  Paris Descartes University  and INSERM. V. K. S. and N. K. S. received their PhD fellowships from the  French Ministry of Research and Innovation. The authors would like to acknowledge  Celine Becker  and  Claire Mader  ( Saints-Pères Animal Facility ) for the breeding and management of the animals used in the study.
They suggested that, if the scientific community is able to (i) overcome the production of large training datasets, (ii) more precisely identify and evaluate the error rates, (iii) scale up automated approaches, and (iv) detect novel species, it will then be possible to initiate the development of a generic automated species identification system that could open up vistas of new opportunities for theoretical and applied work in biological and related fields. Since the question raised by Gaston and O'Neill  [10] , automated species identification: why not?, a lot of work has been done on the topic (e. g. [27, 5, 35, 34, 12, 31, 22] ) and it is still attracting much research today, in particular in deep learning  [11, 13, 28]. In order to measure the progress made in a sustainable and repeatable way, the LifeCLEF 9  research platform was created in 2014 as a continuation of the plant identification task  [20]  that was run within the ImageCLEF lab 10  the three years before  [18, 19, 17].
LifeCLEF enlarged the evaluated challenge by considering animals in addition to plants, and audio and video contents in addition to images. In 2018, a new challenge dedicated to the location-based prediction of species was finally introduced (GeoLifeCLEF). The main novelties of the 2019 edition of LifeCLEF compared to the previous year are the following: 1. PlantCLEF focus on tropical flora: The main novelty of the 2019 edition of PlantCLEF is to focus the challenge on the flora of data deficient tropical regions, i. e. regions having the richest biodiversity but for which data availability is much lower than northern countries. 2. Big soundscape data for BirdCLEF: The main novelty of the 2019 edition of BirdCLEF is the introduction of a very large dataset of 350 hours of manually annotated soundscape recordings in addition to the historical mono-species recordings provided by the Xeno-canto community.
3. New data and evaluation metric for GeoLifeCLEF: The 2019 edition of the GeoLifeCLEF challenge tackles some of the methodological weaknesses that were revealed by the pilot 2018 edition and introduces a new big dataset fixing some issues of the previous one. About 250 researchers or students registered to at least one of the three challenges of the lab and 16 of them finally crossed the finish line by completing runs and participating in the collaborative evaluation. In the following sections, we provide a synthesis of the methodology and main results of each of the three challenges of LifeCLEF2019. More details can be found in the overview reports of each challenge and the individual reports of the participants (references provided below).
Dataset and Evaluation Protocol We provided a new training data set of 10K species mainly focused on the Guiana shield and the Amazon rain forest, known to be the largest collection of living plants and animal species in the world (see figure1). As for the previous two years, this training data was mainly aggregated by querying popular image search engines with the binomial Latin name of the targeted species. We actually did show in previous editions of LifeCLEF that training deep learning models on such noisy big data is as effective as training models on cleaner but smaller expert data  [14] ,  [15]. The average number of images per species in that new data set is much lower than the data set used in the previous editions of PlantCLEF (about 1 vs. 3). Many species contain only a few images and some of them might even contain only 1 image, making a much more challenging task.