dataesr/scientific-paragraphs-categorization huggingface dataset:
Applications of EcoPAD (v1. 0) to manipulative experiments and observation sites Broadly speaking, data-model integration stands to increase the overall precision and accuracy of model-based experimentation  (Luo et al. , 2011b; Niu et al. , 2014). Systems for which data have been collected in the field and that are well represented by ecological models therefore have the capacity to receive the highest benefits from EcoPAD (v1. 0) to improve forecasts. In a global change context, experimental manipulations, including ecosystem responses to changes in precipitation regimes, carbon dioxide concentrations, temperatures, season lengths, and species compositional shifts, can now be assimilated into ecosystem models  (Shi et al. , 2016; Xu et al. , 2006; Gao et al. , 2011; Lebauer et al. , 2013).
0) works hand-in-hand between modelers and experimenters in the life cycle of the SPRUCE project. The EcoPAD-SPRUCE system operates while experimenters are making measurements or planning for future research. Information is constantly fed back between modelers and experimenters, and simultaneous efforts from both parties illustrate how communication between models and data advance and shape our understanding towards better forecasts during the life cycle of a scientific project. ModEx can be extended to other experimental systems to do the following: (1) predict what an ecosystem's response might be to treatments once the experimenter has selected a site and decided the experimental plan; (2) assimilate the data experimenters are collecting during the experiment to constrain model predictions; (3) project what an ecosystem's responses may likely be in the rest of the experiment; (4) tell experimenters which important datasets they may want to collect in order to understand the system; (5) periodically update the projections; and (6) improve the models, the data assimilation system, and field experiments during the process.
In addition to manipulative experiments, the data assimilation system of EcoPAD (v1. 0) can be used for automated model calibration for FLUXNET sites or other observation networks, such as NEON and LTER  (Johnson et al. , 2010; Robertson et al. , 2012). The application of EcoPAD (v1. 0) at FLUXNET, NEON, or LTER sites includes three steps in general. First, build the climate forcing in the suitable formats of EcoPAD (v1. 0) from the database of each site. Second, collect the prior information (include observations of state variables) in the data assimilation system from FLUXNET, NEON, or LTER sites. Third, incorporate the forcing and prior information into EcoPAD (v1. 0) and then run EcoPAD (v1. 0) with the dynamic data assimilation system. Furthermore, the proposed continental-scale ecology study  (Schimel, 2011) , EcoPAD (v1.
Future developments EcoPAD (v1. 0) will expand as time goes on. The system is designed to incorporate multiple process-based models, diverse data assimilation techniques, and various ecological state variables for different ecosystems. The case studies presented in earlier sections are based primarily on one model. A multiple (or ensemble) model approach is helpful in tracking uncertainty sources from our process understanding. With rapidly evolving ecological knowledge, emerging models with different hypotheses, such as the microbialenzyme model  (Wieder et al. , 2013) , enhance our capacity in ecological prediction but can also benefit from rapid tests against data if incorporated into EcoPAD (v1. 0). In addition to MCMC  (Braswell et al. , 2005; Xu et al. , 2006) , a variety of data assimilation techniques have been recently applied to improve models for ecological forecasting, such as the EnKF  (Gao et al.
, 2011) , genetic algorithms  (Zhou and Luo, 2008) , and 4-D variational assimilation  (Peylin et al. , 2016). Future development will incorporate different optimization techniques to offer users the option to search for the best model parameters by selecting and comparing the best method for their specific studies. We focus mostly on carbonrelated state variables in the SPRUCE example, and the data assimilation system in EcoPAD (v1. 0) needs to include more observed variables for constraining model parameters. For example, the NEON sites not only provide measured ecosystem CO 2 fluxes and soil carbon stocks, but also resource (e. g. , GPP-transpiration for water and GPP-intercepted PAR for light) use efficiency  (Johnson et al. , 2010). Researchers interested in creating their own multiple model and/or multiple assimilation scheme version of Eco-PAD (v1.
0) can start from the GitHub repository (https: //github. com/ou-ecolab, last access: January 2019) where the Geosci. Model Dev. ,  12, [1119] [1120] [1121] [1122] [1123] [1124] [1125] [1126] [1127] [1128] [1129] [1130] [1131] [1132] [1133] [1134] [1135] [1136] [1137] 2019  www. geosci-model-dev. net/12/1119/2019/ source code of the EcoPAD (v1. 0) workflow is archived. To add a new variable that is not forecasted in the EcoPAD-SPRUCE example requires modelers and experimenters to work together to understand their process-based model, observations, and how messaging works in the workflow of EcoPAD (v1. 0) following the example of EcoPAD-SPRUCE. To add a new model or a new data assimilation scheme for variables that are forecasted in EcoPAD-SPRUCE, researchers need to create additional dockers and mount them to the existing workflow with knowledge of how information is passed within the workflow (see the Supplement for detailed information).
With these improvements, one goal of EcoPAD (v1. 0) is to enable the research community to understand and reduce forecasting uncertainties from different sources and forecast various aspects of future biogeochemical and ecological changes as data become available. EcoPAD (v1. 0) acts as a tool to link model and data, not as a substitution for either model or data. Ecological forecasting through Eco-PAD (v1. 0) relies strongly on theoretical (model) and empirical (data) ecological studies. Questions regarding the major factors regulating temporal variability in methane emissions cannot be directly answered by EcoPAD (v1. 0). How to make use of EcoPAD (v1. 0) to inspire breakthroughs in both theoretical and empirical ecological studies is worth future exploration. The power of EcoPAD (v1. 0) also lies in the potential service it can bring to society.
Conclusions The fully interactive web-based Ecological Platform for Assimilating Data (EcoPAD, v1. 0) into models aims to promote data-model integration towards predictive ecology by making complex ecosystem model and data assimilation techniques accessible to different audience. It is supported by meta-databases of biogeochemical variables, libraries of modules for process models, a toolbox of inversion techniques, and a scalable scientific workflow. Through these components, it automates data management, model simulation, data assimilation, ecological forecasting, and result visualization, providing an open, convenient, transparent, flex-ible, scalable, traceable, and readily portable platform to systematically conduct data-model integration towards better ecological forecasting. We illustrated several of its functionalities through the Spruce and Peatland Responses Under Climatic and Environmental change (SPRUCE) experiment.
The iterative forecasting approach from EcoPAD-SPRUCE through the prediction-question-discussion-adjustment-prediction cycle and extensive communication between model and data creates a new paradigm to best inform forecasting. In addition to forecasting, EcoPAD (v1. 0) enables an interactive web-based approach to conduct model simulation, estimate model parameters or state variables, quantify uncertainty in estimated parameters and projected states of ecosystems, evaluate model structures, and assess sampling strategies. Altogether, EcoPAD-SPRUCE creates a smart interactive model-experiment (ModEx) system from which experimenters can know what an ecosystem's response might be at the beginning of their experiments, constrain models through collected measurements, predict an ecosystem's response in the rest of the experiments, adjust measurements to better understand their system, periodically update projections, and improve models, the data assimilation system, and field experiments.
Specifically for forecasting carbon dynamics, EcoPAD-SPRUCE revealed that better forecasting relies on improvements in model structure, parameterization, and accurate external forcing. Accurate external forcing is critical for shortterm flux-based carbon predictions, while the right process understanding, parameterization, and historical information are essential for long-term pool-based predictions. In addition, EcoPAD (v1. 0) provides an avenue to disentangle different sources of uncertainties in carbon cycling studies and to provide reliable forecasts with accountable uncertainties. search related to methane. PJH and DR were in charge of observational data collection, transfer, and archiving. VS and CGJ maintained and made further improvements to the system. YH conducted the analysis and drafted the paper.
Figure3. The scientific workflow of EcoPAD (v1. 0). The workflow wraps ecological models and data assimilation algorithms with the docker containerization platform. Users trigger different tasks through the representational state transfer (RESTful) application-programming interface (API). Tasks are managed through the asynchronous task queue, Celery. Tasks can be executed concurrently on a single or more worker servers across different scalable IT infrastructures. MongoDB is a database software that takes charge of data management in EcoPAD (v1. 0), and RabbitMQ is a message broker.
Acknowledgements. SPRUCE components of this work (PJH, DMR) are based upon work supported by the  U. S. Department of Energy, Office of Science, Office of Biological and Environmental Research. Oak Ridge National Laboratory  is managed by  UT-Battelle, LLC , for the  U. S. Department of Energy  under contract  DE-AC05-00OR22725. Review statement. This paper was edited by  Philippe Peylin  and reviewed by two anonymous referees.
Code availability. The EcoPAD (v1. 0) portal is available at https: //ecolab. nau. edu/ecopad_portal/ (Jiang et al. , 2019a), and code is provided at the GitHub repository (https://github. com/ou-ecolab, Jiang et al. , 2019b). Data availability. Relevant data for this paper are available at the SPRUCE project web page (https://mnspruce. ornl. gov/, last access: January 2019) and the EcoPAD (v1. 0) web portal (https://ecolab. nau. edu/ecopad_portal/, Jiang et al. , 2019a). Additional data can be requested from the corresponding author. Supplement. The supplement related to this article is available online at: https://doi. org/10. 5194/gmd-12-1119-2019-supplement.
The velocity then decreases quickly down to almost 0 away from this corridor. The depth is fixed to 0. 1 cm, the flow rate at the inlet is set to 5 NmL•min -1 and the pressure at the outlet is maintained at atmospheric pressure. The velocity scale is in cm•s -1 and is the same for insets. In red are represented areas with a velocity superior or equal to 0. 5 cm•s -1. Both chips are 86 cm long and 49 cm wide. These simulations were performed using the Autodesk CFD (Computational Fluid Dynamics) software. In order to increase the cross-section area without creating large dead volumes, it has been chosen to combine both aforementioned solutions and split the flow into numerous parallel buffer tanks before collecting all the fluid back into one single outlet. Figure  2b  shows an example with 4 buffer tanks.
1 cm, the flow rate at the inlet is set to 5 NmL•min -1 and the pressure at the outlet is maintained at atmospheric pressure. The velocity scale is in cm•s -1 and is the same for insets. In red are represented areas with a velocity superior or equal to 0. 5 cm•s -1. Both chips are 86 cm long and 49 cm wide. These simulations were performed using the Autodesk CFD (Computational Fluid Dynamics) software. In order to increase the cross-section area without creating large dead volumes, it has been chosen to combine both aforementioned solutions and split the flow into numerous parallel buffer tanks before collecting all the fluid back into one single outlet. Figure  2b  shows an example with 4 buffer tanks. The total cross-section area and the length of each of them are equal to those of the buffer tank presented in Figure  2a  but the width of each buffer tank is 4 times smaller.
Methodology for Simulation of the Gas Flow and Mixing Several simulations have been made to determine the impact of the number of stages and the flow rate variations over the homogeneity and the time needed to achieve a steady state at the outlet. These simulations have been conducted for a compressible gas phase using the Autodesk CFD (Computational Fluid Dynamics) software and all the parameters used, and their values are listed in Considering that a single chip is made of 2 stages, the total volume of one double side chip (2 on Figure  4 ) including channels and buffer tanks is 3. 372 mL, increasing the mixing capacity to mix pulses of gas A of t A = 1 s with pulses of gas B of t B = 9 s at a theoretical flow rate Q = 10 NmL•min -1.
Methodology for Simulation of the Gas Flow and Mixing Several simulations have been made to determine the impact of the number of stages and the flow rate variations over the homogeneity and the time needed to achieve a steady state at the outlet. These simulations have been conducted for a compressible gas phase using the Autodesk CFD (Computational Fluid Dynamics) software and all the parameters used, and their values are listed in Table  S1. The gases used for these simulations were air and formaldehyde. The temperature was considered constant all over the micromixer and equal to 23 • C. Reynolds number was calculated to be 110 at the inlet of the micromixer, which has the lowest section and highest flow rate (100 NmL•min -1 ) in the entire micromixer (1 mm 2 ). This indicates that the flow is laminar in the whole system.
- 5 17 Figure 2. 5172 Figure 2. Velocity amplitude of a 3 cm long and 4 cm wide buffer tank (a) and of a set of 4 buffer tanks in parallel of 3 cm long and 1 cm wide each (b). The depth is fixed to 0. 1 cm, the flow rate at the inlet is set to 5 NmL•min -1 and the pressure at the outlet is maintained at atmospheric pressure. The velocity scale is in cm•s -1 and is the same for insets. In red are represented areas with a velocity superior or equal to 0. 5 cm•s -1. Both chips are 86 cm long and 49 cm wide. These simulations were performed using the Autodesk CFD (Computational Fluid Dynamics) software.
The following contribution describes a neural net-based, noninvasive methodology for electroencephalographic (EEG) signal classification. The application concerns a brain-computer interface (BCI) allowing disabled people to interact with their environment using only brain activity. It consists of classifying user's thoughts in order to translate them into commands, such as controlling wheelchairs, cursor movement, or spelling. The proposed method follows a functional model, as is the case for any BCI, and can be achieved through three main phases: data acquisition and preprocessing, feature extraction, and classification of brains activities. For this purpose, we propose an interpretation model implementing a quantization method using both fast Fourier transform with root mean square error for feature extraction and a self-organizing-map-based neural network to generate classifiers, allowing better interpretation of brain activities.
Similar to LDA, the support vector machine classifier (SVM)  [18]  provides linear and nonlinear methods and maximizes the distance between the nearest neighborhood samples. Moreover, knowing that EEG signals are very complicated physiological signals to describe, and that feature vectors are not linearly separable, the use of neural networks (NN) seems to be a good candidate for solving many nonlinear problems in BCI research. Thus, with their learning capabilities, they are suitable for data classification and for the multiclass process according to the desired architecture  [19, 20]. Obtained data from the electrical activity generated by brain structures have different characteristics. The overlapping aspect of electrical activities is due to several million neurons crossing several layers of the brain to reach the scalp.
It makes it possible to classify the user's wishes using a training procedure depending on data characteristics  [6, 14]. Linear discriminant analysis (LDA)  [17] , which is commonly used for the BCI system, is suitable for real time implementation and requires only a few computational resources. Similar to LDA, the support vector machine classifier (SVM)  [18]  provides linear and nonlinear methods and maximizes the distance between the nearest neighborhood samples. Moreover, knowing that EEG signals are very complicated physiological signals to describe, and that feature vectors are not linearly separable, the use of neural networks (NN) seems to be a good candidate for solving many nonlinear problems in BCI research. Thus, with their learning capabilities, they are suitable for data classification and for the multiclass process according to the desired architecture  [19, 20].
All these combinations give straightforward labels for all electrode positions. However, as for the 10-20 standard, no rule is complete without some exceptions. After putting channels on the helmet, a set of measures should be considered to secure experimentations. A ground channel was used in order to measure brain activities, and for proper processing and analysis of the diseases related to the brain, a reference channel was used to reduce the noise ratio or artifacts of EEG recordings by applying a filtering system. An OpenVibe application was used as a tool for data acquisition, processing, classification, and visualization of brain signals. It is an open source software platform. Table  1  illustrates the nomenclature for the electrode locations corresponding to the name of each location.
Brains Activity Classification Classification of brain activities aims to discriminate different tasks according to data provided during the feature extraction phase. For this purpose, we propose an adaptive classifier for mental tasks that employs two phases of implementation: learning and clustering. The learning phase implements a learning process based on a self-organizing map (SOM) and computes data according to a random and cyclic algorithm, which begins in our case by initializing whole activities associated with each BCI sensor. Developed by Kohonen, SOM is organized around a set of interconnected processing neurons intended for competing signals  [24]. The SOM network belongs to the category of unsupervised learning networks, and does not require any human intervention. As is illustrated in Figure  5 , it can map the input data space spanned by (x 1 , • • • , x n ) onto a one-or a two-dimensional array of neurons.
The masks are constructed based on the known class of the training dataset. After SOM is trained with the dataset, the features belonging to a specific activity are presented to the network and the corresponding outputs for that set is then summed up. Results can be viewed as a matrix (Figure  14 ), in order to illustrate the amount of activation of the individual neurons in a specific class. These results show the effectiveness of the proposed approach throughout the data processing chain. They are very promising results. For example, the activity "converse" involves all parts of the brain at different intensities, confirming the results shown below for the corresponding red colors that appear in all SOM nets. The "watching a video" activity involves the occipital and temporal lobes, represented here by the color green.
Indeed, channels 10 (PO8) and 16 (T8), located in the occipital and temporal lobes, respectively, confirm this, as shown in Figure  4. The activation of SOM for the whole training dataset does not provide much information, as the data for the five classes are mixed. These data start to make sense when they are separated according to the classes, as shown in Figure  14. The training dataset was chosen for this figure in order to have a figure with different colors. These tasks form five probabilities. Some neurons are active only in a selected class; these neurons have a probability of 1 for that class and zero for the others. On the other hand, some neurons are not active in any of the three classes; these neurons have zero probability for all classes. The above results were obtained by subdividing the dataset into two bases: training and test bases, with a rate of 70% and 30%, respectively.
The activation of SOM for the whole training dataset does not provide much information, as the data for the five classes are mixed. These data start to make sense when they are separated according to the classes, as shown in Figure  14. The training dataset was chosen for this figure in order to have a figure with different colors. These tasks form five probabilities. Some neurons are active only in a selected class; these neurons have a probability of 1 for that class and zero for the others. On the other hand, some neurons are not active in any of the three classes; these neurons have zero probability for all classes. The above results were obtained by subdividing the dataset into two bases: training and test bases, with a rate of 70% and 30%, respectively. For each subject, more than 20,480 training samples were processed, namely 14336 samples for training and 6144 samples for the test.
Conclusion In this paper, we experimented with a training protocol for EEG signal classification. For this purpose, 10 subjects aged between 20 and 26 years old were selected. Five activities were considered: conversing, reading, working, watching a video, and resting. Noninvasive EEG methods were implemented for 2 minutes each to extract cerebral activities, and the acquired data were subjected to treatments in order to allow classification, resulting in a BCI system that can identify different activities. To improve the classification mechanism, we proposed a classification model based on the SOM neural network, whose inputs represent the whole brain activities recorded by 16 sensors placed on the skull according to the 10-20 System. In this work, artifacts and noise of the extracted EEG signals were filtered by a FFT-based quantization method and characteristics were extracted using a RMSE model.
Conclusions In this paper, we experimented with a training protocol for EEG signal classification. For this purpose, 10 subjects aged between 20 and 26 years old were selected. Five activities were considered: conversing, reading, working, watching a video, and resting. Noninvasive EEG methods were implemented for 2 minutes each to extract cerebral activities, and the acquired data were subjected to treatments in order to allow classification, resulting in a BCI system that can identify different activities. To improve the classification mechanism, we proposed a classification model based on the SOM neural network, whose inputs represent the whole brain activities recorded by 16 sensors placed on the skull according to the 10-20 System. In this work, artifacts and noise of the extracted EEG signals were filtered by a FFT-based quantization method and characteristics were extracted using a RMSE model.
Introduction Software tools used for bioimage analysis tend to be seen as utilities that solve problems off-the-shelf. The extreme version of such is like: "If I know where to click, I can get good results!". In case of gaming software, as the user gets more used to the software, the user can achieve the final stage faster. To some extent, this might be true also with bioimage analysis software, but there is a big difference. As bioimage analysis is a part of scientific research, the goal to achieve is not to clear the common final stage that everyone heads toward, but something original that others have not found out. The difficulty of the usage of bioimage analysis software does not only reside in the hidden commands, but also in the fact that the user needs to come up with more-or-less original analysis.