dataesr/scientific-paragraphs-categorization huggingface dataset:
Example workflow: Optimization of a hippocampal pyramidal cell model We shall go through an entire single neuron model building workflow, in order to demonstrate the functionalities, flexibility, benefits, ease of use of the HHNB. While the tools and software packages needed for optimizing a single cell NEURON model already exist, and are freely available online (e. g. , eFEL, BluePyEfe, BluePyOpt; see sections "Feature extraction, Model selection, Optimization job submission, Optimized model fetching and analysis, Model simulation run and registration in the Model Catalog"), these resources are not natively glued together in an integrated environment and would require the development of ad hoc code and visualization frameworks for the completion of a whole workflow. Conversely, thanks to the HHNB, the users will just go through point-andclick actions, thus facilitating the exploration of different data/model coupling, the investigation of optimal HPC resource settings, and the reproducibility of the optimization workflows.
Discussion In the framework of the HBP  (Amunts et al. , 2016)  and the EBRAINS research infrastructure, 16 we have developed an online resource for the optimization of biophysically detailed single cell NEURON models  (Hines and Carnevale, 1997)  based on experimental results. The HHNB allows users, via a user-friendly GUI, to go through an entire model building workflow that includes: (1) The extraction of electrophysiological features from a dataset of traces or from recordings provided by the user; (2) the choice of a model to be optimized or the upload of well-described model files built by the user; (3) the optimization of the model based on the extracted features; the interface allows the configuration of the parameters related to the genetic algorithm underlying the optimization as well as the specification of the HPC system resources to be 16 https://ebrains.
eu/ used for running the process; (4) the simulation of the optimized model via a graphical interactive interface, which offers functionalities for setting simulation and stimulus parameters as well as visualizing and downloading the simulated neural activity. The HHNB adds unique features to the ecosystem of tools and platforms for neural data analysis and simulation. Thanks to the integration of several software packages and online web applications in a user-friendly online environment, the HHNB offers a plethora of functionalities that usually require distinct workflows and/or stages of application execution, software installation and data homogenization. The feature extraction is run using the NFE resource  (Bologna et al. , 2021) , embedded in the HHNB and residing in the same VM as the HHNB so as to allow a seamless communication between the two web applications and faster data and result file management.
Data available in the NFE are hosted in the EBRAINS KG or in public EBRAINS data containers. The NFE exploits the BluePyEfe and the eFEL Python libraries (see section "Methods") by hiding from the user the technical details of configuration file writing and data management (as required when both tools are run in a standalone manner on a local machine) and exposing a user-friendly point-and-click interface, instead. The models available for the optimization are fetched from the EBRAINS Model Catalog (see Table  1 ), which provides details and links related to the modeling work carried out in the framework of the HBP/EBRAINS research infrastructure as well as results concerning the validation of models against experimental observations  (Sáray et al. , 2021). A tight integration is also in place between the HHNB and the HPC systems available for job optimization: via the UNICORE Python library or dedicated APIs for the interaction with the CSCS-DAINT system and the NSG, respectively, and using an intuitive web interface, the HHNB seamlessly allows to authenticate to the remote platforms, configure the job execution files and fetch the optimization results.
This will allow to flawlessly reproduce users' workflows for data comparison and validation or further analysis. Finally, we plan to strengthen the collaborative aspect of the HHNB by creating a seamless integration with the EBRAINS Collaboratory environment. 17 The EBRAINS Collaboratory provides a framework to create and share documents, tools, code and applications via dedicated workspaces called Collabs, which are individually linked to a data drive 18  that can be used as a common repository. In order to provide the scientific community with a resource able to foster research collaboration, we plan to leverage the storage space of the EBRAINS Collaboratory and the services it offers for saving and sharing the HHNB relevant data and metadata files, to allow partners and collaborator to visualize, download, finalize or modify existing or newly created HHNB workflows.
Software resources Name References Url Usage in the HHNB BluePyEfe N/A https://github. com/BlueBrain/BluePyEfe Feature extraction workflow (eFEL Python wrapper) BluePyOpt Van Geit et al. , 2016 https://github. com/BlueBrain/BluePyOpt Single cell model optimization on HPC systems CINECA N/A https://www. cineca. it/ Hosting of the Virtual Machines where the HHNB and the Service Account Utility are installed CSCS N/A https://www. cscs. ch/ HPC system where the optimizations are run EBRAINS Model Catalog N/A https://model-catalog. brainsimulation. eu/ Hosting of the single cell models chosen by the users for optimization EBRAINS This paper https://github. com/ebrains-cls- N/A Hodgkin-Huxley Neuron interactive/hbp-bsp-hh-neuron-builder Builder EBRAINS Bologna et al. , 2021 https: Feature extraction workflow (web NeuroFeatureExtract //github.
com/ebrains-cls-interactive/hbp- interface) bsp-hh-neuron-builder/tree/master/efelg eFEL N/A https://github. com/BlueBrain/eFEL Feature extraction workflow (core package) NEURON Carnevale and Hines, 2006 https://www. neuron. yale. edu Model building Neuroscience Gateway Sivagnanam et al. , 2013 http://www. nsgportal. org HPC system where the optimizations are run Service Account Utility N/A https://github. com/ebrains-clsinteractive/eb-clsi-service-account Optimization job submission on behalf of users with no credentials on HPC systems.
selection (CovSel) Response-oriented sequential alternation (ROSA) A B S T R A C T Multi-block datasets are widely met in the chemometrics domain, and several data fusion approaches have recently been proposed to treat them. Apart from exploratory and predictive modelling, a key task in this context is feature selection which involves finding key complementary variables across multiple data blocks that jointly provide a good explanation of the response variables, revealing the key variables of the system. In that direction, a new method called response-oriented covariate selection (ROCS) is proposed here. ROCS is a direct extension of the covariance selection (CovSel) approach to multi-block scenarios, where the choice is based on a competition between variables in different blocks, as is done in the response-oriented sequential alternation (ROSA) method.
Introduction In the domain of analytical chemistry, there is now a growing interest in combining different sensor modalities to better understand the samples or to achieve better predictive models, as in many cases a single modality carries only partial information about the response variables  [1] [2] [3]. For example, jointly using different spectral sensing modes such as near-infrared, mid-infrared, and Raman spectroscopy  [4]  leads to multiple data matrices, that is multi-block datasets. In fact, multi-block datasets can have data from any analytical technique which generates multivariate signals and meta-data coming from any other knowledge about the samples, as variety, origin, physical structure, etc. More details on multi-block data and modelling techniques in the chemometric domain can be found in Refs.
[2, 3]. To analyse multi-block datasets, several latent space based approaches are available; for example, the multi-block variants of traditional principal component analysis (MB-PCA) and partial least-squares regression (MB-PLS) can be used for exploratory and predictive data modelling  [5] [6] [7] , respectively. However, multi-block extensions such as MB-PCA and MB-PLS are highly scale-dependent and may not be of use in cases where the scales of data blocks are different  [8] [9] [10] [11]. Several approaches to pre-process the datasets in order to "equalize" their range of variability have been proposed  [12] , but it is still a challenge to find the best normalization approaches for data with different scales. Data with different scales here means data with either different signal intensity ranges or with differences in total amount of variability.
A wide range of applications can be found in the scientific literature on the use of SO-PLS and PO-PLS, ranging from predictive modelling such as classification  [13]  and regression  [10] , as well to their extensions for pre-processing ensembles such as sequential  [14]  and parallel  [15]  pre-processing through orthogonalization. The main benefit of the SO-PLS model is when the data block order is meaningful and known so that the important data blocks can be modelled first and the less useful ones later  [10]. The data block order is not a problem for PO-PLS approaches in terms of extracting common and distinct information from different data blocks  [10, 11]. Although both the sequential and parallel PLS based approaches have shown promising potential to analyse multi-block datasets, there is still a big challenge related to the optimisation of such models.
Prediction of moisture content in pear fruit by fusing information from two portable spectrometers The dataset consisted of spectral measurements performed on 240 pear fruit samples using two portable spectrometers. The pear dataset was used to show the capability of ROCS to extract variables from spectral fingerprints. More details on samples and the experimental setup can be found in an earlier study  [24]. Spectral measurements were performed on intact fruit first using a portable visible and near-infrared (Vis-NIR) spectrometer Felix F-750 (Camas, WA, USA) and then using a short-wave infrared (SWIR) dynamic light projection (DLP) spectrometer NIR Scan Nano (Texas Instrument, USA). After the spectral measurements, a cylindrical disc was cut out at the position of the largest diameter of the fruit and divided into four equal quadrants.
Spectral data from different sampling forms of rice The dataset consisted of NIR spectral measurements performed on 200 rice samples, in three different forms i. e. , rice with coat, rice kernel and rice flour  [25]. The same samples were used for all three measurements; hence, the data becomes a three-block dataset. At first, the spectral measurements were performed on rice with coat, then on the rice kernel and then on the rice flour of the same kernels. The spectra were acquired with a multi-purpose analyzer (MPA) Fourier transform near-infrared (FT-NIR) (Bruker, Germany) spectrometer. The reference property was the protein content, which according to the primary study was measured using the Dumas combustion method  [25].
Reflection and transmission measurements on milk to predict fat content The dataset consisted of 300 milk samples from 300 cows from all over Flanders (Belgium)  [26]. The dataset was used to show the potential of the ROCS method to use the complementary information in the reflection and transmission data to predict milk fat content. The dataset consisted of Vis-NIR (306. 5-1710. 9 nm) data measured with a Zeiss Corona 45 VISNIR 1. 7 diode array spectrometer. The Vis-NIR (350-2500 nm) transmission measurements were performed with a LabSpec spectrophotometer from ASD. The reference measurement for fat content was performed within the 3 days following the spectral measurements  [26].
Data analysis ROCS, just like CovSel  [22] , extracts variables from multi-block datasets. Selected variables can later be used for any kind of analysis, for example regression or classification. The possibility of predictive modelling on the extracted variables has already been proven in earlier works  [22, 23]. Since ROCS is a special case of the ROSA  [16]  modelling approach, the performances of ROSA and ROCS models were compared using the three presented data sets. Furthermore, the performance comparison of the models using variables selected by ROCS and SO-CovSel is presented. The ROCS and ROSA optimal models were calculated with a 5-fold cross-validation to judge the optimal number of variables for ROCS and latent variables for ROSA. For SO-CovSel, the optimal number of variables was determined by exploring all combinations of variables in the range of [0-20] using a 5-fold cross-validation.
However, the approach has the drawbacks that it is time consuming, particularly when the number of blocks is large, and it requires blocks to be ordered beforehand. On the contrary, ROCS uses a faster heuristic based on parallel competition between blocks, however, with drawback of being more prone to falling into a local minimum. To demonstrate these differences, the ROCS and SO-CovSel analyses were performed on the pear dataset where the aim was to use NIR and SWIR data to predict MC in intact pear fruit. Fig. 1A  shows the explained variance in the response variable obtained with ROCS and SO-CovSel. The number of variables selected by SO-CovSel and ROCS to explain up to 90% of variance in the response variable were different, where SO-CovSel selected only 9 variables from the NIR data block (Fig.
To build on the findings from the pear data analysis, the SO-CovSel and ROCS data analyses were also performed on the rice and the milk data sets. The results are shown in Table  1. ROCS either extracted the same number of variables as SO-CovSel or more. However, the optimal PLS models were achieved with fewer latent variables than extracted variables. The performances of the final models for both ROCS and SO-CovSel for the rice and milk data sets were similar. Some results from the ROCS and SO-CovSel analyses of the rice data set suggested that the optimal physical form of the rice samples was the rice flour as all the variables were selected from the corresponding data block. For milk data set, the selection of variables from both the reflection and transmission data blocks suggests that the two modes play a complementary role in predicting fat content in milk.
Research is needed to adapt ROSA's block selection work to with multiple responses. The key point in this step is the definition of residuals, which can be done with many combinations of the Y columns. The interaction between how the residuals is calculated and how ROSA works needs to be studied in detail. This is however beyond the scope of this paper. Furthermore, since ROSA and ROCS are sensitive to local minima, as they use a simple stepwise heuristic, more research should be carried out to modify this heuristic to cope with this problem. In ROCS, the multiblock information is handled by selecting a winner using the minimum residuals at a particular step. One can assume that if multiple blocks at a particular step have similar residuals, then the winner selection is based on the order in which the blocks carrying similar residuals are arranged (particularly in MATLAB implementation).
This is also one of the drawbacks of ROSA strategy which is the backbone of ROCS. To be cautious for such cases, as described in ROSA method, it is advised to keep the record of residuals for each block at each step of the ROCS algorithm run. Later, such record of residuals (using plots) can help in diagnosing cases where blocks may have similar residuals. In the current demonstration of ROCS, all the datasets were spectral data sets which have comparable scales. The final predictive MLR and PLSR models were built directly by concatenating the variables selected from the different data blocks. However, one can assume that if the scales of data varies then the user need to be cautious while building the final predictive models. In that case, the user can build the final predictive models by either using autoscaling of the data or by using scale independent modelling techniques such as SO-PLS or ROSA.
Conclusions A new multi-block method called response-oriented covariates selection (ROCS) was presented. The new method is a block order-and scale-independent technique which can handle many data blocks when selecting variables. The evaluation of the method on real multi-block datasets showed that the method was able to capture chemically relevant features from the different blocks. Furthermore, the selected features were highly predictive of the response variables. The selected features can then be used for a range of tasks such as regression and classification. The applications showed that multi-block variable selection can be used to answer several research questions such as finding the best physical forms of samples for spectral measurements, selecting optimal spectral sensors for dedicated applications, and combining different optical geometries of spectroscopy to predict sample properties.
Model intercomparison studies are carried out to test and compare the simulated outputs of various model setups over the same study domain. The Great Lakes region is such a domain of high public interest as it not only resembles a challenging region to model with its transboundary location, strong lake effects, and regions of strong human impact but is also one of the most densely populated areas in the USA and Canada. This study brought together a wide range of researchers setting up their models of choice in a highly standardized experimental setup using the same geophysical datasets, forcings, common routing product, and locations of performance evaluation across the 1×10 6 km 2 study domain. The study comprises 13 models covering a wide range of model types from machine-learning-based, basinwise, subbasin-based, and gridded models that are either locally or globally calibrated or calibrated for one of each of the six predefined regions of the watershed.
Unlike most hydrologically focused model intercomparisons, this study not only compares models regarding their capability to simulate streamflow (Q) but also evaluates the quality of simulated actual evapotranspiration (AET), surface soil moisture (SSM), and snow water equivalent (SWE). The latter three outputs are compared against gridded reference datasets. The comparisons are performed in two ways -either by aggregating model outputs and the reference to basin level or by regridding all model outputs to the reference grid and comparing the model simulations at each grid-cell. The main results of this study are as follows: 1. The comparison of models regarding streamflow reveals the superior quality of the machine-learning-based model in the performance of all experiments; even for the most challenging spatiotemporal validation, the machine learning (ML) model outperforms any other physically based model.
3. The regionally calibrated models -while losing less performance in spatial and spatiotemporal validation than locally calibrated models -exhibit low performances in highly regulated and urban areas and agricultural regions in the USA. 4. Comparisons of additional model outputs (AET, SSM, and SWE) against gridded reference datasets show that aggregating model outputs and the reference dataset to the basin scale can lead to different conclusions than a comparison at the native grid scale. The latter is deemed preferable, especially for variables with large spatial variability such as SWE. 5. A multi-objective-based analysis of the model performances across all variables (Q, AET, SSM, and SWE) reveals overall well-performing locally calibrated models (i. e. , HYMOD2-lumped) and regionally calibrated models (i.
, 2019). In particular, the recent successful application of data-driven models in hydrologic applications necessitate standardized experiments -such as model intercomparison studies -to make sure that the models are using the same information and strategies to enable fair comparisons. Several model intercomparison have been carried out over subdomains (here called regions) of the Great Lakes in the past  (Fry et al. , 2014; Gaborit et al. , 2017a; Mai et al. , 2021)  under the umbrella of the Great Lakes Runoff Intercomparison Projects (GRIP). The GRIP projects were envisioned since the Great Lakes region with its transboundary location leads to (a) challenges such as finding datasets that are consistent across borders or models that are only set up on either side of the border, (b) challenges in the modeling itself due to, for example, climatic conditions driven by large lake effects and substantial areas of heavy agricultural land use and urban areas, and (c) a high public interest since the Great Lakes watershed is one of the most densely populated areas in Canada (32 % of the population) and the USA (8 % of the population; Michigan Sea Grant, 2022).
In particular, the last study (GRIP-E by  Mai et al. , 2021)  listed the following challenges in the concluding remarks: I. There was only a short period of forcings (5 years) to calibrate and evaluate the models. II. There was only a comparison of streamflow but no additional variables like soil moisture. III. Even though all models use the same forcings, all other datasets (soil texture, vegetation, basin delineation, etc. ) were not standardized. Hence, model differences might be due to different quality input data but not due to differences in models themselves. The study presented here will address these previous GRIP study limitations. Furthermore, this study demonstrates some unique intercomparison aspects that are not necessarily common practice in large-scale hydrologic model intercomparison studies.
Key noteworthy aspects of this intercomparison study are as follows: 1. Datasets used. Hydrologic and land surface models need data to be set up (e. g. , soil and land cover) and to be forced (e. g. , with precipitation). The forcing data have, at best, a high temporal and spatial resolution, a complete coverage of the domain of interest, a long available time period, and no missing data. In this study, models will be using common datasets for these forcings, and all geophysical datasets are required to set up the various kinds of models. A recently developed 18year-long forcing dataset will be employed  (Gasset et al. , 2021). This addresses limitations (I) and (III) above. 2. Variables compared. Models are traditionally only evaluated for one primary variable (e. g. , streamflow). In this study, models will be evaluated regarding variables beyond streamflow such as evapotranspiration, soil moisture, and snow water equivalent.
In this study, we shared data through an interactive website, which was first internally accessible during the study and then publicly shared to enhance data sharing, exploring, and communication with a broader audience. The remainder of the paper is structured as follows. Section 2 presents the materials used and methods applied including a description of datasets, models, metrics, and types of comparative analyses. Section 3 describes and discusses the results of the analyses performed, while Sect. 4 summarizes the conclusions that can be drawn from the experiments performed here. Note that this work is accompanied by an extensive Supplement, primarily providing more details for model setups, and an interactive web-site (http://www. hydrohub. org/mips_introduction. html, last access: 6 July 2022)  (Mai, 2022) , for sharing and exploring comparative results.