dataesr/scientific-paragraphs-categorization huggingface dataset:
The graphical user interfaces are often not compatible with the type of remote computing offered by cloud technologies and the large collections contain largely overlapping components that are however not interoperable with each other. These collections therefore do not offer a unified and granular way of describing an image processing workflow. This situation also often requires users to learn multiple platforms to be able to complete their workflows. Code notebooks such as CodeOcean capsules or Jupyter notebooks offers also an easy access to cloud computing or HPC, but several aspect of workflow management are also left to the user, in particular data provenance. At the same time that the field is shifting to running workflows in the cloud or high performance computing environments, there also comes the need to run more complex workflows integrating tools and data coming from different life science fields, such as genomics or proteomics data, or spatial transcriptomics.
This containerization can be performed at the level of each individual workflow component (such as in Galaxy  30, 31  ), or for complete workflows (such as in BIAFLOWS  21  or coming to grand-challenges). Biocontainers 32 is proposing a standard and recipes for these containerizations, as well as a marketplace for the containers, today mostly for -omics data processing. As a community we need to join this effort for a better exploitation and reproducibility by other communities of the imaging data produced by our workflows. One of the particularities of workflows in bioimage analysis is the need for visual and accurate feedback at critical workflow steps. This human-in-the-loop requirement has so far prevented the community from using SWMS more widely. But this is now changing as image processing tools and visual feedback are now getting incorporated in SWMS.
21, 31, 33 wards findability and accessibility of image analysis workflows At a general level in life-science and not specifically for the bioimage analysis community, coordination efforts are ongoing in the direction of the "FAIRification" of workflows, but also the ease to access HPC resources to run them. They are led by European Research Infrastructures, such as ELIXIR. 34 ELIXIR is an intergovernmental organization that aims to coordinate the resources offered nationally for databases, software tools and access to cloud storage and HPCs, and associated training material. BIII, the finder tool mentioned above, is now for example part of the recommended interoperability resources. EOSC-Life is an ESFRI cluster project involving the 13 biomedical research infrastructures whose goal is to create an open, digital and collaborative space for biological and medical research in the European Open Science Cloud.
This includes making image data and image processing and analysis workflows compliant with the FAIR principles, while enabling interoperability with tools and data from other life science domains as mandated by the European Commission. Galaxy  30  has been identified and selected as an aggregator of communities, and selected by EOSC-Life as an exemplary workflow management system that promotes cross-communities interoperability in the cloud. This does not mean that the bioimage analysis community needs to restrict to this particular choice, but it means that the workflows have to be compatible with this choice and to prepare for a future where local compute resources will not anymore be used to run a workflow. To ease this interoperability, a common description needs to be defined, in order to be able to make workflows interoperable and compatible with different infrastructure environments.
org controlled vocabularies or Bioschemas, a specific extension for Life Science resources. Galaxy is one of many SWMS, a more exhaustive list curated by the Common Workflow Language organization can be found here. Table  3  focuses on SWMS used in the bioimage analysis field, and it details their specificities. These specificities tend to support the message that the effort should not be in trying to push the implementation of workflows in only one solution, but rather to allow and ease the portability of workflows in multiple frameworks and execution environments, an approach supported by initiatives CWL. We then argue that these standards are key to facilitate the workflow ecosystems and further promote open and reproducible sciences.
Conclusion The field of bioimage analysis, partly thanks to the NEUBIAS community, has been recently consolidated. Its community has contributed to the emergence of new tools to find, launch, compare and learn how to use and customize image analysis workflows. We believe that today the field has become mature enough to contribute to the general open science effort in life science and to enable better access to data and computational resources. This effort should help promote workflow sharing and reuse and a wider data integration and interoperability. We deeply encourage the bioimage analyst community, and by extension the associated software developer community, to sustain this effort and to rely on these tools. In particular, we encourage bioimage analysts to describe their workflows thoroughly by following the CWL standard, index them in BIII, and share them in SWMS such as BIAFLOWS compatible with Galaxy.
Grant information: PPG and VB acknowledge  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INBS-04 ). AG and  HM acknowledge l'Institut Français de Bioinformatique  supported by the  French National Research Agency  ( ANR-11-INBS-0013 ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Understanding the relationships between biological processes is paramount to unravel pathophysiological mechanisms. These relationships can be modeled with Transfer Functions (TFs), with no need of a priori hypotheses as to the shape of the transfer function. Here we present Iliski, a software dedicated to TFs computation between two signals. It includes different pre-treatment routines and TF computation processes: deconvolution, deterministic and non-deterministic optimization algorithms that are adapted to disparate datasets. We apply Iliski to data on neurovascular coupling, an ensemble of cellular mechanisms that link neuronal activity to local changes of blood flow, highlighting the software benefits and caveats in the computation and evaluation of TFs. We also propose a workflow that will help users to choose the best computation according to the dataset.
We noticed that there is no standard software package providing all these different TF extraction tools, nor a program where all these approaches are available in a comfortable signal pre-treatment and I/O workflow. Here, we present Iliski ([ɪlɪʃkɪ], meaning "relationship" in Turkish), a software which contains all the functionalities that we previously used  (Aydin et al. )  and which, being open source, can be further improved by the users. Although Iliski was initially thought to help data analysis in Biology, its features make it suitable for diverse applications  [26, 27].
Data loading and pre-treatment We propose two input files format: either plain text files or HDF5 data, the latter being an open-source file format with advanced database features. As experimental acquisitions are prone to multiple component noise, we provided, as an option to the analysis workflow, smoothing (Savitzky-Golay method) and median filter functions, to exclude outliers. The input and output signals are interpolated to a chosen time interval (Δt). Both signals can be cut between two given time points to study continuous recordings while computing TFs on chunks of signal (Fig  1C ). As an option, boxcar function of variable duration can be used in place of the input signal. Note that Iliski was not coded to handle complex-valued signals.
TF computation options Two main types of TF computation are proposed: deconvolution or function optimization. The former is straightforward, either Toeplitz or Fourier deconvolution, and does not require any specific settings. The latter is the optimization of a parametric function, which requires further settings depending on the chosen algorithm. Beside the proposed fitting functions, the users can input their own function in the graphical interface or add it to the default ones by modifying a text file (the procedure is described in the Iliski Manual). The TF dimension is user-defined, with setting of the TF duration and 'Sampling Time' parameters that match the original data or can be augmented by non-linear interpolation. Optimization of parameters can be done with various Matlab algorithms, each coming with pros and cons (see Results section) (Fig  1C, middle ).
Evaluation of the TF accuracy A TF is evaluated comparing its prediction-the convolution of the input signal and the TF-to the expected output. Two metrics are used in Iliski: the Pearson coefficient (corrcoef function, Matlab, Fig 1C  , right ) and the residual sum of squares. The former was chosen to have a metric solely focusing on the dynamic, allowing for inter-subject comparisons, while the latter evaluates the overall fit, considering the amplitude of the prediction. The cost function of all the optimization algorithms tested in this article is the residual sum of squares (hereinafter referred to as "residuals").
Implementation Iliski is accessible both as a GUI and as a set of functions to be used in scripts. It has been developed using Matlab R2018a, with the following dependencies: Optimization Toolbox, Signal Processing Toolbox and Global Optimization Toolbox. Common user errors are thoroughly prevented by various messages and fail safes. In parallel, all errors are treated and saved in a log file, to allow for efficient bug-fixing by any developer. We purposely kept just a few parameters to modify through the GUI, with the goal of providing an easy-to-use tool for people not used to these functions. In most cases, Matlab default parameters of each deconvolution/optimization function worked well with our data, and we believe that it can be extended to many biological datasets. However, a user skilled with Matlab and optimization algorithms can easily modify the parameters of each function used.
Its four parameters are not all independent from one another, e. g. , p 1 , p 2 and p 4 all impact the TF amplitude. This inter-dependency between the parameters brings an ill-posed optimization problem with multiple local minima of the cost function, the sum of the residual squares, in the 4D space of the parameters. To tackle the function optimization problem, we chose standard algorithms (all implemented in the Optimization Toolbox of Matlab) to encompass the main available options. A derivative-free optimization method  [6]  is provided by the fminsearch function in Matlab, which uses the Nelder-Mead simplex algorithm. This approach on our data produced a TF with more than one underivable point that is not representative of the smooth dynamic of neurovascular coupling. Another common option is provided by Quasi-Newton optimization algorithms, which uses an approximation of the Jacobian: for this approach too, we tested an unconstrained built-in method (fminunc function, Matlab).
In Aydin et al. (2020), we described a workflow of runs and iterations to get to biologically consistent TFs (see Supplementary Fig  1  in Aydin et al. [25] ). To speed up computation, we imposed bounds over the parameters. Note that such bounds can be set through the Iliski GUI for any constrainable algorithm. Using our data, Simulated Annealing gave a smooth TF and a prediction as good as fminsearch for the onset phase of the vascular response. The data shown in The non-deterministic, Simulated Annealing algorithm with subsequent iterations method allows to efficiently exclude these TFs and obtain the best trade-off between prediction performance and biological consistency at the cost of a longer computation time. Direct deconvolution is a good option when the goal is the prediction quality within the training database.
Evaluating the number of runs in a non-deterministic case As already mentioned, the Simulated Annealing algorithm requires several runs and iterations to obtain a good TF, where 'run' means a single optimization and 'iteration' an ensemble of runs sharing the same initial values of the fitting function. In our experience, starting the optimization with a 'bad' TF-whose shape is different from what is expected for the processed dataset-helps to collect more local minima in a pool of optimization runs. For example, in our previous study  [25] , we proposed iterations of 50 runs and started with the initial values of the standard TF (one Γ HRF) which, peaking at 5 seconds, turned to be much slower than any of the optimized TFs. The sequence of 50-runs iterations stopped when, within an iteration, no clear improvement was found in the optimized TF  [25].
On average, 2 iterations were sufficient to get a stable TF with Pearson coefficient above 0. 9. Here, we investigated if a higher number of runs is beneficial to the detection of the minimum of the cost function and if it prevents the need for further iterations. We compared 50 and 200 runs with single and double iterations, in cascade (Fig  4A ). In a mouse dataset, we observed a non-significant trend towards more scattered TFs shapes for computation using 50 runs versus 200 runs (1-way ANOVA, F(3, 16) = 2. 086, p = 0. 14, Fig  4B ). Similarly, the quality of the TFs did not Odor stimulation elicited a neuronal response in the Olfactory Bulb of a mouse, reported by a calcium-dependent fluorescent signal (in blue, left panel), providing the input of TF computation. Output is given by the vascular response, measured as the change in speed of red blood cells flowing inside a capillary proximal to the recorded neuronal activation (in yellow, right panel).
Both experimental data have been resampled at 50ms and used to compute a set of TFs (in orange) either with direct deconvolution approaches (Fourier or Toeplitz methods, middleupper panel TFs) or with 1-Γ function optimization performed by 3 different algorithms (middle-lower panel TFs). Complex TFs bring accurate prediction but amplify the noise of the data used to deconvolve them, with a consequent loss of robustness on other datasets. Smoother TFs are less accurate on the training dataset, but much robust when applied to test datasets. https://doi. org/10. 1371/journal. pcbi. 1008614. g002.
PLOS COMPUTATIONAL BIOLOGY significantly improve with increasing runs (1-way ANOVA, F (3, 16) = 2. 299, p = 0. 12). As a result, TFs with fast dynamics (peaking within 1 and 2 sec), was a common feature independently of the adopted protocol (Fig  4C ). In a dataset from another mouse (Fig  4D  and 4E ), TFs with sparse time to peak values after 200 runs improved after a second iteration, with the same number of runs (2. 3 ± 0. 3 s VS 1. 5 ± 0. 1 s (mean ± SEM), two-tailed T-test, unpaired, p = 0. 02 < 0. 05). Note that this compression of TF dynamics was not accompanied by a significant improvement of the TF quality (residuals: 10. 1 ± 2. 1 vs. 6. 9 ± 0. 8 (mean ± SEM) for 200 and 200 + 200 runs respectively, two-tailed T-test, unpaired, p = 0. 19). To conclude, depending on the input/output signals, non-deterministic algorithms can produce TFs with different dynamics but close performances in the prediction.
Discussion Iliski provides a user friendly, interactive, and rich in options software for quickly testing different methods and settings to compute TFs between biological processes. In addition to its standard integrated functions, it also allows for user-defined functions of any number of parameters and the possibility of replacing an input signal with a boxcar function, enlarges its usage. Using data from the NVC field, we demonstrate how critical is the choice of the method for computing TFs and the caveats of parameters such as the number of iterations necessary to non-deterministic algorithms. Note that we did not report the influence of smoothing, interpolation, fitting and cost functions choice that are also known to affect the result. The use of multimodal datasets, i. e. , neuronal calcium signal, measurements of vascular responses at both the microscopic and mesoscopic scales enabled us to demonstrate that NVC is represented by a similar TF which is much faster than the classical HRF, a finding which is getting accepted in the field of brain imaging based on blood flow  [5, 25, 32, 33].
Availability and future directions Iliski is open-source and freely available under the Creative Commons Attribution 4. 0 International (CC BY 4. 0) license. Iliski is maintained on GitLab, enabling user-friendly bug report and community work to make the tool fit the users' need. It can be found here: https://github. com/alike-aydin/Iliski. In the neurovascular imaging field, computing the hemodynamic response function is paramount to interpreting vascular activation in terms of neural activation. In any other field, computing TFs may be of help to go deeper in the interpretation of the results. For these reasons, we think it is extremely helpful to have a data analysis tool which lets fast testing of different algorithms with a user-friendly interface.
Financial support was provided by the  Institut National de la Santeet de la Recherche Medicale (INSERM) , the  Fondation pour la Recherche Medicale  (https://www. frm. org/, EQU201903007811 ) to SC, the  Agence Nationale de la Recherche  (https://anr. fr/, ANR/NSF 15-NEUC-0003-02 , ANR/TF-fUS-CADASIL and NR- 16-RHUS-0004  [ RHU TRT_cSVD ]) to SC, the  Fondation Leducq Transatlantic Networks of Excellence program  (https://www. fondationleducq. org/,  16CVD05 , Understanding the role of the perivascular space in cerebral small vessel disease) to SC, the  IHU FOReSIGHT  [ ANR-18-IAHU-0001 ] supported by  French  state funds managed by the  Agence Nationale de la Recherche  within the  Investissements d'Avenir program  to SC and the  NIH  (https://www. nih. gov/, R01NS078168 ) to PJD. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
1a ). Each station was equipped with an infra-red sensor for CO 2 (model Gascard NG, range 0-5000 ppmv, accuracy ±2%), two electrochemical sensors for H 2 S and SO 2 (City Technology, sensor type T3ST/F for SO 2 , range 0-50 ppmv, accuracy of ±2%, resolution 0. 1 ppmv, and TC4F-1A for H 2 S, range 0-100, accuracy of ±2%, resolution 0. 1 ppmv). Each sensor was calibrated using standard gas mixtures in the laboratory of the local volcanic observatory (OVSG-IPGP) before installation. A Campbell Scientific CR6 data-logger followed a duty cycle schedule and recorded data at 1 Hz for a 20 min-long period every six hours, data were periodically transmitted to the OVSG. All sensors and data-logger were housed inside a water-proof aluminium box (33 × 23 × 18 cm), with the external volcanic gas-air mixture pumped at ~1.
Testing the model performance Mass consistent wind models, such as DWM  (Douglas et al. , 1990)  may produce highly-resolved surface-level wind analyses through surface measurements, which are strongly influenced by the topography, especially in the immediate vicinity of the station where the topographic reliefs change very sharply. These models are generally more representative during the daytime and in topography simple terrain  (Stohl et al. , 1997). However, upper-level analyses often provide poor resolutions due to the lack of vertical wind information. This problem is generally overcome by using numerical weather models or radiosondes. Hence to understand which input data are more appropriate to obtain a reliable wind field, we carried out two tests by using local meteorological data only (Test 1) and ERA5 reanalysis dataset (Copernicus Climate Change Service, 2017; Test 2) in the DWM  (Douglas et al.
, 1990). Local meteorological data were collected data from the Piton Sanner station operated by OVSG-IPGP and located on the summit dome, at ca. 1447 m (Fig. 1b ). This station is equipped with anemometer (05103-03 Campbell), a widevane (05103-03 Campbell) and an atmospheric barometer (PTB101B Campbell). The meteorological data used in the DWM are the surface temperature and the wind components (Test 1). The same type of data are acquired by ERA5 reanalysis dataset (Test 2). It is worth noting that atmospheric stability information (i. e. , the domain-averaged temperature lapse-rate for each hour of the simulation day (K/m) required in DWM is only taken from the ERA5 reanalysis dataset. To test the impact of uncertainty on topography resolution, we verified whether the fit between simulation results and observations is preserved when using a lower-resolution DEM (25 m; www.
In both cases, the hourly gas concentrations were stored at the tracking points. We considered the water vapour as the simulated plume component since it prevails in all gas mixtures  (Tamburello et al. , 2019). To do so, the values of the water vapour fluxes (which are assumed constant on a given simulated day) were randomly sampled from the Empirical Cumulative Distribution Functions (ECDFs) reported in Appendix B, built on the data available from the literature on this observable  (Allard et al. , 2014; Tamburello et al. , 2019). The ECDFs were assumed representative of the variability in the source fluxes in the selected period, and daily fluxes were assumed independent of each other. In Table  1  we provided the complete dataset of gas fluxes used as input parameters of each fumarole (G56, TAS, CS) for each day of simulation.
Simulation workflow We developed an automatized workflow which uses Python and Bash scripts to: i) download ERA5 reanalysis data and/or pre-process weather data, (i. e. from a weather station in the domain) and successively process these in the DWM (through a meteorological processor called DIAGNO;  Douglas et al. , 1990)  to obtain the wind field needed by the numerical model; ii) randomly assign the emission rates to the investigated sources on the basis of the ECDFs built from literature data; iii) perform the simulations; iv) post-process the results to obtain contour plots of gas concentration. The workflow starts defining the input meteorological data. As mentioned above, we defined three configurations for the workflow (Tests 1, 2 and 3). In the first configuration, we used the data collected from the Piton Sanner station (Fig.
1b ). These data reproduce accurately the local time-averaged wind field with a 1 h time resolution, but they describe one specific point only (the station) and do not spatially constrain the DWM model. In Test 2, we took into account the data from ERA5 dataset, which covers the Earth on a 30-km grid and resolve the atmosphere using 37 levels from the surface up to a height of 80 km. We first interpolated ERA5 weather data to the location of interest, then we collected wind data for 13 levels up to 500 m above the ground, along with the parameters relative to the stability conditions of the atmosphere. Also in this case the time-step acquisition is 1 h. It is worth noting that, in all simulations, the information regarding the atmospheric stability needed for the DWM (i. e. the domain-averaged temperature lapse-rate in K/km, specified for 24 h of simulation) derives from the ERA5 dataset.
In Appendix A, we provided the hourly averages of wind direction and intensity from both the Piton Sanner station and ERA5 dataset. To evaluate the wind field over the computational domain, we run the DWM, which uses a terrain-following coordinate system. It needs topography data, average wind on the computational domain, and atmospheric stability information within the scale of the domain (i. e. the temperature gradient in K/m). The wind field was taken to be divergence free, which approximation is generally applicable up to a kilometre above ground level  (Dutton and Fichtl, 1969). Furthermore, since terrain-following coordinates were used, this approximation still holds even if ground level varies significantly within the computational domain. The wind field was then linearly interpolated into the computational grid.