dataesr/scientific-paragraphs-categorization huggingface dataset:
01-6. 75 ng mL -1 and 3. 06-20. 48 ng/mL -1 , respectively. While for the DDA dataset, LOD and LOQ were found in the range of 3. 3-8. 9 ng mL -1 and 10. 2-27. 0 ng mL -1 , respectively. We then found LOD and LOQ of MS2 data of the DIA mode to select one fragment of relatively high abundance for quantification. In DIA MS2, 5 out of 10 drugs (ranolazine, diphenhydramine, oxytetracycline, finasteride, and dipyridamole) showed lower LOD and LOQ than their MS1 level. The LOD in DIA mode at MS2 level was found to range between 0. 27 and 11. 15 ng mL -1. All LOD and LOQ values at MS1/MS2 levels of all drugs are summarized in Table  1. MS2 level of quantification is considered more selective and precise as compared to MS1 level  [12]. These results indicated better reproducibility and sensitivity in DIA mode by combining MS1/MS2 scans in Skyline.
Untargeted and targeted extraction of multiple precursor/fragment ion transitions in Skyline allows better analysis and quantification of desired metabolites  [12]. The detection and clustering of all drugs with their MS1 and MS2 datasets represent drug intensities in each sample by three replicates on all calibrated points using Perseus software, as shown in Figure  5. The clustering of serum metabolites with replicates in three acquisition modes on MS1/MS2 levels are presented in the heatmap shown in Figure  6. We compared the sensitivity, detection limit, and reproducibility of three modes (i. e. , MS1 full-scan, DDA, and DIA) on MS1 and MS2 levels for DIA data Skyline. Response.
Discussion In a recent study, Klont et al. [1]  have developed a DIA strategy employed for drug analysis in biological fluids on a Q-TOF instrument, providing a new choice for drug metabolomics. In another study Pierre, et al. [13]  have developed a workflow on an Orbitrap FusionTM mass spectrometer to compare the efficiency and complementarity of DDA and DIA modes in human plasma samples using MS-DIAL. We proposed a pipeline consisting of three well-known software for mass spectrometry data interpretations, MS-DIAL, Skyline, and Perseus. We examined three acquisition modes using their capabilities to identify a wide range of drugs or metabolites and quantify them in the complex fluid using open-access software. We suggest the use of MS-DIAL software for feature generation, annotation, identification, and MS2 database search followed by a comprehensive transition list generation using MS1 full-scan and DDA mode in MS-DIAL.
This transition list can be uploaded to Skyline for quantification in DIA mode. Perseus software can be used for statistical analysis of drug standards and metabolites in QC sample using the efficiency of MS1 full-scan, DDA, and DIA modes. In recent comparative studies between full-scan, DDA, and DIA mass spectrometry acquisitions, Guo et al. [14]  provided valuable information about maximum features generation, and spectral quality using MS-DIAL software. They suggested that both MS1 and DDA data are efficiently processed by the MS-DIAL; however, in DIA mode the amount of noise was found to be higher than DDA and was not able to efficiently deconvolute the DIA data leading to the serious limitation in the analysis. In our experience of processing data in a DIA workflow using MS-DIAL, this resulted in very high noise in MS2 spectra leading to poor MS2 deconvolution and quality of spectra.
The novelty of the proposed study is to generate a wide range of metabolome transition lists for identification and quantification of metabolites in DIA mode on the MS2 level. The developed transition lists of metabolites have minimized the chance of false positive results in DIA analysis. There are some studies reported regarding the comparison between DDAand DIA-based mass spectrometry for metabolomics. Some software solutions are also discussed for handling wide range spectral data (MS1/MS2) obtained from DIA as well as DDA workflows such as MS-DIAL, Skyline, Mzmine, and GNPS. No comparative study has been carried out to objectively assess the advantages and limitations of these different tools  [14] [15] [16] [17] [18]. For feature generation, annotation, and identification of metabolites, many researchers are using MS-DIAL software.
In our case, MS-DIAL was used for MS1 full-scan, and DDA mode analysis, but was not found suitable for DIA MS2 deconvolution results. However, Skyline is a very efficient choice for DIA deconvolution for MS2 spectra in many samples as compared to other software results. Quantification information is important for method validation and reproducibility. In our experiments, the DIA method showed the best quantitative results and the lowest detection limit on MS1 and MS2 levels, followed by full-scan and DDA on the MS1 level. As reported by Guo et al. [14]  both MS1 and DIA were better for feature generation than DDA due to loss in intensity at the MS1 level; our results also confirmed this issue. However, it is not clear how at the MS1 level intensities were higher in DIA than MS1 and DDA modes.
Quantification at the MS2 level provides an additional advantage for coeluting isobaric and high background metabolites by using specific transition for quantitative analysis. This can be useful for both DDA and DIA modes, but for the comprehensive MS2 data, DDA is generally employed with exclusion criteria. This allows deeper MS2 data for low-abundance metabolites. However, MS2 data cannot be used for quantitative analysis. DIA is thus superior as it can be used for quantification of both MS1 and MS2 levels. The peak areas from Skyline can easily be used for statistical analysis using Perseus software. As Perseus is designed for proteomics data, unlike other commercial software it is optimized for use with large numbers of variables and very powerful software for statistical analysis of high-dimensional data; thus, is also well suited for metabolomics data.
Software and Data Processing The MS1 and DDA data acquired for the QC pool sample in raw data (. d) format was first converted into the Analysis Base File (Abf. ) format. MS-DIAL software (version 4. 24) was used for feature detection, ion species annotation, compound spectra extraction, and peak alignment between samples and spectral analysis resulting in the transition list containing information from every detected feature such as retention time and precursor m/z, MS2 ions  [10]. Identification of drugs and serum metabolites in the complex mixture was undertaken in MS-DIAL using MS/MS libraries (MS/MS-Pos containing 13,303 unique compounds with 290,915 records) which are openly available. The MS-DIAL parameters were set as follows: retention time 1-13 min; mass range 70-1200 Da; MS1 and MS2 tolerance were 0.
01 and 0. 05 Da, respectively; minimum peak width, 5 scans; minimum peak height, 1000 amplitude; smoothing level, 2; MS/MS abundance cut off, 10 amplitude; mass slice width, 0. 1 Da; exclusion mass list, none; retention time tolerance, 2 min; MS1 accurate mass tolerance, 0. 01 Da; MS2 accurate mass tolerance, 0. 05 Da; identification score cut off, 70%. Spectral library, (MS/MS-pos) LC-MS/MS in MSP format. For data-independent acquisition, all parameters were the same except for the experimental file containing the DIA window mass range Q1 window of 25 and 50 Da; (Table  S1 ) minimum peak height, 1000; minimum peak width, 4; MS/MS abundance cut off, 5 amplitude. Skyline (version 21. 2. 0. 425) is open-source software well known in proteomics for its unique features, especially visualization and advanced method refinement options such as retention time scheduling and reproducibility  [20, 21].
These features are now also implemented for small molecule workflows such as pharmaceuticals (drug metabolites and toxicology), metabolomics, forensics, and food safety  [22]. The Skyline parameters were set as follows: TOF for precursor mass analyzer, resolving power: 30,000; mass range: 70-1200 Da; retention time filtering: included all matching scans for MS1 and DDA modes. All parameters remained the same in the DIA mode except for an isolation window with the gap of 50 and 25 Da. covering a mass range of 99. 5-1200 Da. for MS2 data. The method has a margin of 0. 5 m/z added to the isolation window, for example, 99. 5-150. 5, 149. 5-200. 5, etc. Perseus (version 1. 6. 2. 1), a comprehensive software for functional analysis of highdimensional proteomics data including data cleansing, preprocessing, exploratory analysis, and statistical methods  [23]  was used.
Conclusions In conclusion, we suggest a pipeline that integrates freely available software, MS-DIAL, Skyline, and Perseus for the metabolomic profiling and drug quantification in DIA-based analysis. This will be helpful for the laboratories with limited bioinformatics support, not requiring vendor-specific software for DIA data analysis. MS-DIAL can be used for comprehensive feature generation and transitions using MS1 and DDA data followed by the DIA data deconvolution using a metabolic library of transitions and precursor masses can be superior to MS1-based analysis. This provides an additional advantage to utilizing both MS1 and MS2 levels of quantification using Skyline. In Skyline, the choice of MS1 and MS2 can be made for the quantification of metabolites by visible inspection, allowing for better quantification performance.
The second step in determining the 'Area of Interest' is to establish the range of damping values to consider. These values are determined by first applying a 3rd-order, low-pass Butterworth filter to the radiation damping data where the cut-off frequency is 20% of the frequency range used to define the width of the 'Area of Interest'. The lower limit of the damping values is set equal to zero, while the upper limit it set equal to the maximum value of the filtered damping. Figure  7  below illustrates an example of the 'Area of Interest' obtained for one data set. 3) Identifying and removing peaks: Two processes are used to identify peaks due to the effects of irregular frequencies within the damping data in order that the phenomena illustrated in Figure  1  and Figures  2  and 3  may be captured and resolved.
Once the peaks in the damping data due to the effects of irregular frequencies have been identified, the peak at the lowest frequency value is first removed. It is then necessary to determine how much damping data from either side of the irregular frequency should be removed. A frequency window of damping values to be removed is determined on the fractional height of the peak (or depth of the trough) to the height of the 'Area of Interest'. Specifically, the window width is set to 10% of the fractional height of the peak multiplied by the frequency width of the 'Area of Interest'. Again, the figure of 10% has been selected based on previous experience. Note that a maximum window to remove is set to 20% of the width of the 'Area of Interest' to prevent too much data being removed. Once the window around an irregular frequency has been removed, the height of the 'Area of Interest' is recalculated using the same process described in Section III-B2.
The general architecture is made up of four layers: data acquisition, data storage, data processing, and application layers. The data sources include real-time sensors, IoT devices, reanalysis data, and raw files. The data are then checked for errors and missing values using a proposed method based on ERA5-Land reanalysis data and deep learning. The resulting coefficient of determination (R 2 ) and Root Mean Squared Error (RMSE) for this method were 0. 96 and 0. 04, respectively, for the scaled air temperature estimate. The MongoDB NoSQL database is used for storage thanks to its ability to deal with real-world big data. The system offers various services such as (i) weather time series forecasts, (ii) visualization and analysis of meteorological data, and (iii) the use of machine learning to estimate the reference evapotranspiration (ET 0 ) needed for efficient irrigation.
Data Acquisition Layer Heterogeneous data from a variety of sources, including meteorological station data, IoT weather sensors, reanalysis data, third-party meteorological services, and raw files (CSV, Excel, etc. ), are used as the input for this layer. The concept of big data is introduced to the field by the volume, velocity, and variety that characterize these data. Missing values are handled in this layer using the method developed in Section 4. 4. 1 prior to being stored in the NoSQL MongoDB database.
Weather Station Data The weather dataset used in this study was collected from a meteorological station installed in the study area (Figure  1 ) at the half-hour scale from 2013 to 2020. The used tower is equipped with different sensors  [27, 28]  to measure: • Incoming solar radiation using (Kipp and Zonen CM5 Pyranometer, Delft, The Netherlands). • Air temperature in Kelvin, relative humidity (R3_Hr, as a fraction between 0 and 1) and vapor pressure by using (HMP45C, Vaisala, Helsinki, Finland). • Wind speed using (A100R Anemometer, R. M. Young Company, Traverse City, MI, USA). • Rainfall using (FSS500 Tipping Bucket Automatic Rain Gauge, Campbell Scientific Inc. , Logan, UT, USA). Next, records are stored in data loggers before being collected manually by agents or sent to a centralized server via a cellular connection.
We downloaded ERA5-Land weather data from 2013 to 2020. Since the cloud service Climate Data Store API (cdsapi) has a limit of 100,000 records per request, we downloaded each year separately and combined them all at the end. The downloaded data were then converted to a pandas dataframe data structure using the Python language. We also developed a function called "get_era5_land_grib_as_dataframe", available as a part of the GIS class of the public library Data Science Toolkit (DST)  [35]  that accepts an ERA5-Land grip file as a parameter and returns a pandas dataframe, which is the most commonly used data structure in the data science field. The full description of the downloaded parameters is described in Table  2. Dewpoint temperature d2m The temperature to which the air, at 2 m above the surface of the Earth, would have to be cooled for saturation to occur.
Data Storage Layer In our system, most of the collected data are time series (meteorological data, reanalysis data, satellite data, etc. ). This type is characterized mainly by variety (multisource), volume, and velocity (each half-hour). To deal with this, the system uses MongoDB, a big-datadriven database for storing and retrieving meteorological data. The choice of using the MongoDB database comes after several strengths it presents and its suitability for our use case, including that it was designed to replace or enhance the classic Relational Database Management Systems (RDBMS), providing it with a variety of additional characteristics such as scalability being schema-less. It is also powerful at handling large amounts of real-time data and efficiently handling memory, as it is written using the C++ programming language.
Deep Learning Models Deep learning models or neural networks have gained success in solving complex tasks that were previously human-specific and have required some level of human intelligence to be solved in different fields. They derive their power by trying to mimic the way the human brain works. They are composed of neurons able to process huge amounts of data in order to map the output from a set of inputs using internal mathematical operations. These neurons are organized into groups called layers and, during the propagation of signals between layers in two senses (Forward and backpropagation), the deep learning network learns to perform tasks. In our case, this is a regression task, where the input is ERA5-Land reanalysis data and the output is meteorological station data.
Time Series Data Imputation Service It is common in real-world meteorological data to have missing values for various reasons. Missing values can be due to a network error or due to technical issues with certain measurement sensors, etc. These missing data can affect the performance of any type of model (machine learning, numerical, physical, etc. ). As such, they need to be identified and handled efficiently during the exploratory data analysis (EDA) and preprocessing stages. There are several techniques for dealing with missing data depending on the use case: • Deletion: Deleting rows or columns with missing values will remove this unwanted type of data from our dataset, but it may drastically reduce the size of the dataset, especially in the context of data scarcity.
• Imputation in time series data: In the case of a time series with a trend and seasonality, missing data can be replaced using seasonal adjustment, such as using the data from the same period of the previous year, which is the case for most weather data. However, this method may not be as efficient due to changes in weather patterns around the world. In contrast, if the time series do not present a trend or a seasonality, it can be treated the same way as imputation for a normal dataset. • Imputation in normal datasets: Replacing it using statistical measures of central tendencies such as the mean, median, or mode of a given window of data that require some assumptions about the distribution type of the data to be efficient. This service proposes an approach based on reanalysis data and artificial intelligence to build models that can learn rules to map ERA5-Land reanalysis data to station meteorological data, which is also useful for the surrounding regions of our study area (Section 3), since we are in relatively homogeneous areas in terms of elevation and climate.
62 and λ = 243. 12 degrees Celsius. For global solar radiation (R3_Rg), the study uses the surface solar radiation downwards (ssrd) as estimator. Since the objective is to predict meteorological station data based only on ERA5-Land data, we evaluated the performance of two deep learning models, namely a Feed Forward Neural Network (FFNN) and a Long Short-Term Memory (LSTM), to predict R3_Tair given t2m, R3_Rg given the ssrd, and R3_Hr given t2m and d2m.
c. Long Short-Term Memory (LSTM): The choice behind using this neural network as a comparison is due to its ability to deal with data that have long-term dependency, which is the case for climate data. LSTM belongs to the Recurrent Neural Networks family (RNN), and therefore also has an internal recurrence, that is, during the learning process, a signal is fed back to a neuron or layer that has already received and processed it (Figure  8 ), as well as its ability to remember data through gated cells, which are a kind of memory that accept values in the interval [0, 1] and are used to decide when the flow of a signal passes through the corresponding neuron. LSTM was first developed to resolve the limitations of the vanishing  [41]  and exploding gradient problems that may occur during the training phase.
For example, two features with different bounds can be brought to a similar scale using decimal scaling (Equation (  11 )) x new = x 10 n (11) Such that n is an integer representing the order of the scalings. • Z-score: This transformation scales the value toward a normal distribution with a zero mean and unit variance using the z-score formula (Equation (  12 )). x new = x -µ σ ( 12 ) such that µ is the mean and σ is the standard deviation of the features' distribution. This method is very efficient for datasets with a Gaussian distribution. In our case, we applied the min-max method (Equation (  10 )). Next, we initialize the hyperparameters of the two proposed architectures (Table  4 ). These parameters are not updated during the learning phase.
The proposed approach follows the steps presented in Figure  9. First, we resampled the air temperature, global solar radiation, air relative humidity, and wind speed to the daily average, and precipitation to the cumulative daily values, and then the missing values were deleted. To select the most important contributor variables to the ET 0 estimate, the method uses a random-forest-based technique that ranks the importance of features based on their occurrences in nodes across all trees: the bar chart (Figure  10 ) shows sorted meteorological variables' importance scores. Next, the dataset containing meteorological data and the corresponding FAO Penman-Monteith ET 0 estimated values are split and fed into the XGBoost model, and the model is then evaluated. The objective of this service is to provide an alternative to the FAO Penman-Monteith calculation procedure by learning the behavior of this procedure using a limited number of climatic variables (Figure  11 ).
Weather Data Analysis and Visualization Service It is well-known that data in their raw form are useless, but the information, knowledge, and wisdom derived from them are not. Moving from one state of data to another is known as Knowledge Discovery in Databases (KDD), which is a subset of the modern data science field and can be performed using various methods, such as CRISP-DM  [44] , which stands for CRoss-Industry Standard Process for Data Mining, or the proposed standard method in  [45]. One example of insights data visualization and analysis which can be given in our use case is shown in Figure  12. This is achieved by following a hybrid methodology that includes the following steps: collecting, storing, cleaning, visualizing, analyzing, and mining. The first three steps are common for all other services available on the platform.
Climatic Parameters Calculation and Estimation We split our dataset into five randomly shuffled parts (five folds). We used one fold for model evaluation and the remaining four to train it. Finally, we assess the model's performance by calculating the regression metrics across the five folds of the dataset (Root Mean Squared Error RMSE and the coefficient of determination R 2 ). According to the results of Table  6 , the main point is not the perfect results obtained by using all parameters as estimators but using only air temperature and global solar radiation (average RMSE = 0. 27 and average R 2 = 0. 93), which gave promising results. This represents a practical datadriven approach for accurately estimating ET 0 by combining two criteria: accuracy of the FAO Penman-Monteith method and using limited and simple-to-obtain meteorological variables (air temperature with global solar radiation or only air temperature).
In future work, the platform will integrate more models, especially those known for their performance in the task of short-term and mid-term weather forecasting, as surveyed in  [50]. This platform is the first incremental step towards our goal of creating a decision support system intended to implement smart agriculture in Morocco by using artificial intelligence and data science to solve real-life problems facing farmers. More studies will be performed to add and optimize different parts of this system. For example, for data storage, we used MongoDB as a database to deal with real-time big data, however, in terms of system scalability or dealing with batch processing or long-running ETL (Extract, Transform, and Load) jobs, other technologies should be considered. A potential candidate for this could be other NoSQL databases  [51]  or the Hadoop ecosystem (Spark, MapReduce, Hive, etc.