dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis The object generated by a Multiple Factor Analysis MFA performed on data of pigments, CHCl 3 and MeOH fractions was used to run a Between Class analysis on R under the package ade4 following the original script available on Github (https://github. com/Hubas-prog/BC-MFA). The result is named BC-MFA. The normality of the data and the residuals distribution was tested using the Shapiro-Wilk test. When residuals followed a normal distribution, a one-way ANOVA was performed to detect significant light effects on pigments, CHCl 3 and MeOH fractions variations. When normality was not verified, the non-parametric Van der Waerden test was performed with the R package agricolae. The metabolites diversity indexes for the 2 fractions was determined with the chemodiv package.
Acknowledgements. We would like to thank  Thorsten Fehr  for organising the  Joint Aeolus Tropical Atlantic Campaign (JATAC) , which 585 made this cross-Atlantic study possible. We would also like to thank the  German Aerospace Center (DLR) , which assisted in the transport of radiosonde material and helium tanks from Germany to  Sal  in Cape Verde. Sincere gratitude also goes to the large teams involved in the radiosonde launches. For the Cape Verdean team, our special thanks go to  Azusa Takeishi ,  Tanguy Jonville  and  Cedric Gacial  for their hard work and dedication. 28 https://doi. org/10. 5194/egusphere-2023-742 Preprint. Discussion started: 3 May 2023 c Author(s) 2023. CC BY 4. 0 License.
Information Sources Our search spanned five databases: Scopus, PubMed, Science Direct, Web of Science, and JuSER, the reference system for publications from the jülich research center (JuSER, 2023). To determine the search terms, we created a sample set of papers focused on air quality prediction using interpretable machine learning. This involved conducting preliminary "scoping searches" on Google Scholar and reviewing previous studies and systematic reviews in the field. From these papers, we identified key words, synonyms, and related terms.
Data Collection Process and Data Items The first author (AH) extracted data from the selected articles, and decisions were checked with the second author (IE). Any discrepancies or disagreements regarding relevant data were resolved through consensus via discussion between the two, or with involvement from other authors (KK, MK) if needed. The selected articles underwent thorough analysis, and the following information was extracted from each: reference, study area, air pollutants of interest, sample size, study objective, model or approach used for prediction, model performance, interpretation methods, and interpretation results.
Bootstrapping technique (BS) The bootstrapping technique (BS) is a statistical method that estimates the sampling distribution of a statistic by repeatedly resampling from the observed data  (James et al. , 2013). In the context of machine learning and feature selection, bootstrapping can be used to assess the importance of variables by resampling the dataset and evaluating its impact on model performance. This approach provides a robust means of determining which features have the greatest influence on a model's predictions. Kleinert et al. (2021)  employed this technique to evaluate the effects of different input variables on the results of ozone forecasts generated by a deep learning model. They accomplished this by randomly selecting one variable at a time, thereby disrupting its temporal structure in the input data.
Local Interpretable Model-agnostic Explanations (LIME) Local interpretable model-agnostic explanations (LIME) is a machine learning technique used to explain predictions made by complex machine learning models in a way that is both interpretable and locally faithful  (Ribeiro et al. , 2016b). Nabavi et al. (2021)  used LIME to understand the behavior of their machine learning model for specific instances, illustrating the local relationship between selected features and individual ozone estimations. LIME starts by selecting a specific instance or data point for which you want to explain the model's prediction. It generates a dataset of perturbed instances by slightly modifying the selected instance. These perturbations may involve making small changes to the features while keeping the label constant.
The machine learning model that needs explanation is then used to make predictions on this perturbed dataset. LIME fits an interpretable, locally weighted model (e. g. , linear regression) to approximate the behavior of the complex model within the vicinity of the selected instance. This local model should be simple and interpretable. The local model provides an interpretable approximation of how the machine learning model behaves for the specific instance, allowing you to understand the importance of different features and how they contribute to the prediction. The coefficients of the local model can be used to assess the importance of different features for predicting the instance  (Molnar, 2022).
Partial Dependence Plot (PDP) Partial dependence plots (PDPs) are ideal for representing how a feature affects prediction on average, as long as the feature being calculated is not correlated with other features  (Molnar, 2022). When features are uncorrelated, a PDP displays how the average prediction in a dataset change as a particular feature changes. The maximum number of features that can be included in a PDP is two, as it is difficult for humans to imagine a world with more than three dimensions. Additionally, the distribution of the features in the PDP is not displayed, which may result in overinterpretation of regions with little to no data  (Molnar, 2022). This issue can be easily solved by showing indicators of the data points on the x-axis or by adding a histogram to the plot.
Therefore, using different metrics may yield distinct rankings of feature importance. PFI introduces randomness through shuffling, and results may vary when repeated  (Molnar, 2022). Features that may seem insignificant in a low accuracy model can be significant in a high accuracy model. Therefore, it's essential to understand the model's effectiveness before assessing feature importance (scikit-learn, 2023). Furthermore, when a feature is permuted and correlated with another in the dataset, the model can still access the permuted one through its correlated feature. This situation may lead to low importance scores for both features, even if they are actually important (scikit-learn, 2023). One approach to address this is by clustering the correlated features and retaining one feature from each cluster.
Bootstrapping technique (BS) Bootstrapping technique assesses the impact of input variables without requiring model retraining, using the same model weights learned from the full dataset for the evaluation  (Kleinert et al. , 2021). Preserving the input variable distribution in bootstrapping helps prevent adverse effects when correlated variables are present. However,  Kleinert et al. (2021)  pointed out that this method may underestimate the impact of a specific variable in the case of correlated input data, as the model tends to focus on the dominant feature, as observed with ozone in their study. It's important to note that this analysis solely evaluates the behavior of their deep learning model and does not assess the real-world impact of these variables on actual ozone formation in the atmosphere.
Stability Feature Selection (SFS) Stability feature selection offers robust feature selection, even in non-ideal conditions. It handles high-dimensional data effectively and enhances model generalization, reducing overfitting. SFS simplifies interpretation by focusing on essential features. It's also less sensitive to regularization choices, ensuring ease of implementation  (Meinshausen and Bühlmann, 2010). SFS drawbacks include computational complexity with large datasets, potential information loss due to the exclusion of valuable features, data quality dependency, and limited noise handling, making it less effective when noise follows a pattern.
Strengths and Limitations of this Review This systematic review is the first to discuss interpretable machine learning models aimed at enhancing model interpretability while maintaining high accuracy in predicting air pollution. It serves as a valuable reference for policy-makers, scientists, and researchers in identifying factors contributing to air pollution and developing effective interventions for improved public health. However, our review has limitations: excluding gray literature increases publication bias risk, limited inclusion of studies from five databases may miss relevant research, and the focus is solely on air pollution prediction. Expanding the review to include other application areas would identify additional relevant tools.
ACKNOWLEDGMENTS We extend our gratitude to  Wael Al-Delaimy  for his valuable feedback and support in reviewing this article. His expertise and insightful comments greatly enhanced the quality of our work. Authors would like to acknowledge the support provided by the  GeoHealth Hub for Climate Change and Health in the Middle East and North Africa. The  GeoHealth Hub  was established through Grant # 5U2RTW012228  and #  U01TW012237  from the  National Institute of Health Fogarty International Center (NIH/FIC). However, this support didn't cover the fees to publish the current paper.
Acknowledgements This work benefited from the financial support of  E2S-UPPA  to the first author. We benefit from the analogue modelling lab set up by F. Odonne [e. g. Dufréchou et al. , 2011], whom we warmly thank. Benjamin Guillaume  and  Gianreto Manatschal  are thanked for initiating us to PIVLAB and Strainmap analyses and discussions about the Alpine case, respectively. We would like to thank also the reviewer  Franck Chanier  and the editor  Laurent Jolivet  for the comments which helped to improve this manuscript.
Data processing Raw data were validated, calibrated and then interpolated  (L'Hégaret et al. , 2022). Interpolation of vertical profiles sampled at different times had to be achieved with caution not to create artificial field. To limit spurious effects, we only performed linear in-Figure  1. Velocity vector field at -300m for one of the subsurface anticyclonic eddy sampled by the RV L'Atalante 38 kHz S-ADCP during the EUREC 4 A-OA oceanographic cruise. The regional bathymetry from the ETOPO2 dataset  (Smith & Sandwell, 1997)  is presented in the background as color shading as well as the estimated center (the orange square) of the eddy computed from the observed velocities using  Nencioli et al. (2008)  method. The colored contours represent the loci of constant tangential velocity. The center is defined as the point where the average radial velocity is minimum.
Acknowledgments This research has been supported by the  European Union 's  Horizon 2020 research 893 and innovation program  under grant agreements no. 817578  (TRIATLAS), the  Centre National d'Etudes Spatiales  through the  TOEddies  and  EUREC4A-OA  projects, the  French national programme LEFE INSU ,  IFREMER , the  French vessel research fleet , the  French research infrastructures AERIS  and  ODATIS ,  IPSL , the  Chaire Chanel programme  of the  Geosciences Department at ENS , and the  EUREC4A-OA JPI Ocean  and Climate manuscript submitted to JGR: Oceans.
Overall, sustained observations from IFCBs and UVPs have been ongoing since 2006/8 respectively, and even track back to 1966 for laboratory-based ZooScan observations from preserved samples  (García-Comas et al. , 2011). Despite such time spans,  Kiko et al. (2022)  only recently published the first curated global dataset of particle size between 64-50,000 µm, obtained from UVP5 measurements solely. This release was facilitated by a collaborative management platform, EcoPart (https://ecopart. obs-vlfr. fr), which enables the collection of count and size information of bulk particles detected by the UVP. This unique platform, along with other collaborative platforms such as EcoTaxa (https://ecotaxa. obs-vlfr. fr), the IFCB dashboards (https://ifcb-data. whoi. edu/dashboard, https://ifcb. caloos.
The Pelagic Size Structure database project With the support of many international data providers, we developed the Pelagic Size Structure database (PSSdb, https: //PSSdb. net) in accordance to FAIR principles  (Wilkinson et al. , 2016)  to provide global datasets of particle and plankton size distributions. Our project capitalizes on largely untapped size structure observations from plankton imaging devices, consistent across the 7-10,000 µm size range  (Romagnan et al. , 2015; Lombard et al. , 2019) , and aims to become a global data source like the NOAA World Ocean Database (https://www. ncei. noaa. gov/products/world-ocean-database) and COPEPOD (https://www. st. nmfs. noaa. gov/copepod). The objectives for PSSdb were both to (1) implement a workflow able to retrieve counts, sizes, and taxonomic information from online imaging data streams to calculate particle size spectra, and to (2) provide multi-level, harmonized products, matching the spatio-temporal resolution of current biogeochemical models.
In this paper, we present the first version of PSSdb instrument-specific datasets, consisting of bulk size spectra and derived parameters (slope, intercept and R 2 ) measured by the IFCB, the UVP and benchtop scanners (e. g. ZooScan) within the epipelagic layer. First, we highlight the large spatio-temporal coverage of our observations, before describing the shape of the size spectra and patterns of their derived parameters. Finally, we discuss how PSSdb provides a way to study the links between plankton community structure and global biogeochemical fluxes, and thus inform the development of biogeochemical and data-driven models.
Materials and methods In the following sections, we first describe the data acquisition and processing steps prior to PSSdb ingestion for the three imaging approaches used for this release (section 2. 1). Then we provide details on the current pipeline for PSSdb ingestion that enables the computation of instrument-specific size spectra currently available at https://doi. org/10. 5281/zenodo. 10150020 (section 2. 2). 2. 1 Acquisition and pre-processing of IFCB, UVP, and scanner imaging datasets.
A1). In   (Moberg and Sosik, 2012) , of individual or multiple (in the case of chain-forming or colonial organisms) Region Of Interests (ROI) are extracted from the masked images (also referred to as blobs) using custom feature extraction Matlab code (code and documentation available at: https://github. com/hsosik/IFCBanalysis/) and can be further used to predict taxonomic annotations  (Sosik and Olson, 2007). Taxonomic annotations were used to remove artifacts before data ingestion into PSSdb, and will allow for further work on taxon-specific data products for future releases.
A first segmentation is performed to separate the ROIs from the background, and extract their morphometric features (see suppl. material of  Gorsky et al. (2010) ), depending on a lower size threshold (370±360 µm in ECD on average) and the mean gray level intensity (default is 243). If necessary, a second segmentation may be done after manually separating overlapping ROIs  (Vandromme et al. , 2012). Once the separation of ROIs is optimal, their corresponding vignettes, along with the automatically generated EcoTaxa table, may be uploaded to EcoTaxa to predict and validate the taxonomic annotations. As a starting point, and for reproducibility, we only ingested datasets uploaded on EcoTaxa, as they can be repeatedly accessed and shared amongst collaborators, notably to assess the annotation status, which is important for ingestion into PSSdb (see section 2.
2. 4). Once datasets are exported from EcoTaxa, we consider the reported size-based fractionation of the net tow sample: if the sample was sieved into separate size fractions after the collection, (i. e. a sample collected with 333µm mesh net that was afterwards sieved through 150µm, 500µm and 1 mm meshes) the size spectra are first calculated for each size fractions based on the dilution factor of the aliquots taken for each sieved sample "acq_sub_part" column in EcoTaxa) and the volume of filtered sea water of the net (as determined by the flowmeter; "sample_tot_vol" column in EcoTaxa), to account for the volume effectively scanned within a size fraction. The total size spectrum is then obtained by summing the fraction-specific spectra, since size fractionated scans originate from the same volume.
PSSdb data pipeline The current PSSdb pipeline is illustrated in Figure  1  and includes 5 major steps: the selection (section 2. 2. 1) and extraction (section 2. 2. 2) of imaging datasets from online data streams, the standardization (section 2. 2. 3) and quality control and of the datasets (section 2. 2. 4), the binning of instrument-specific datafiles (section 2. 2. 5), and lastly, the computation of particle size spectra and derived parameters (section 2. 2. 6).
Selection of imaging data streams The first objective of the Pelagic Size Structure database (PSSdb) is to estimate particle size spectra from plankton imaging devices, following the FAIR principles. We thus relied primarily on online and accessible platforms created by the instrument developers to manage their datasets: IFCB dashboards (of generation 2 exclusively, as generation 1 does not include metadata like longitude and latitude) and EcoTaxa/EcoPart, developed for ZooScan and UVP, but also for IFCB and other imaging systems, since a few IFCB datasets ingested in PSSdb were available on EcoTaxa. IFCB dashboards are deployed by individual labs or regional networks with specific urls and are publicly accessible. Conversely, EcoTaxa datasets are not accessible by default, so data providers who wanted to contribute to the PSSdb were asked to provide access to their projects.
Both IFCB dashboards and EcoTaxa contain sample metadata, raw images, their related morphometric measurements and optionally, their taxonomic annotation. To ensure that size distributions were representative of living and non-living particles and excluded methodological artefacts, we selected datasets with predicted and/or curated image classification. Of the 37 datasets on the IFCB dashboards and the 3,290 UVP, scanner, and IFCB datasets on EcoTaxa (last checked on Oct 2023), only 6 projects from IFCB dashboards and 250 projects from EcoTaxa were downloaded and integrated into the first PSSdb release products. The list of the datasets (and their urls) that are included in the first PSSdb release can be downloaded at https://doi. org/10. 5281/zenodo. 10150020, in the "data sources" spreadsheet included in the compressed release datafiles.
Extraction of imaging data streams All functions to list (funcs_list_projects. py) and export (funcs_export_projects. py) datasets from IFCB dashboards and EcoTaxa/EcoPart automatically are available at https://github. com/jessluo/PSSdb/tree/main/scripts. To export IFCB datasets, sample-specific queries to the IFCB dashboards are executed sequentially to retrieve sample metadata such as location, time, and depth, plus the morphometric measurements of individual ROIs stored in the "features" files, and the top 5 taxonomic predictions, stored in "autoclass" files. Metadata, feature, and autoclass files are then combined in a single master table, with a row for each ROI, and saved into multiple files comprising ∼ 500,000 rows to limit the size of the exports and the processing time. Scanner and UVP datasets were automatically exported from EcoTaxa using the API with the default option.
This option retrieves all the information relative to individual ROIs (e. g. area, taxonomic annotation) and samples (e. g. location, depth, time), as well as specific acquisition (e. g. size fraction scanned and associated volume), and processing (e. g. pixel calibration factor) steps. To further retrieve the size and count information of small particles processed by UVPs in real time, which are only uploaded to EcoPart, we wrote a custom script based on existing web scraping python modules. We selected the "raw" export option for all datasets hosted on EcoPart, rather than the default export option which provides summary statistics, consisting of the summed particle counts and biovolume in individual size bins, computed in 5 m depth bins. With the raw export option, we were able to retrieve the number of particles (column "nbr") of a given pixel-based size measurement (column "area"), as well as the number of individual image frames (column "imgcount", used to calculate the cumulative volume) in 1 m depth bins.
This strategy has multiple advantages, as it allows the conversion of pixels to metric area estimates using either the power-law function described in section 2. 1. 2 or a fixed pixel size. It also allows for the construction of size spectra using custom size bins, and for an assessment of the uncertainty of the size spectra estimates, using the bootstrap approach published by  Schartau et al. (2010). Using a pair of identifiers allowing to link each UVP dataset uploaded on EcoPart to its corresponding EcoTaxa ID, the datasets on both platforms were consolidated into a single table to account for all particles detected by the UVP. Since EcoPart "raw" datafiles are summarized in 1 m depth bins, it is impossible to link a specific area estimate to the corresponding EcoTaxa vignette, and thus, its taxonomic annotation.
Standardization of imaging datasets Since raw datasets exported from the API queries are generated with different formats, with specific headers and units, we developed instrument-specific "standardizer" spreadsheets in order to re-format all datasets to the same standard. Each spreadsheet contains the dataset IDs for a given instrument, including the pair of IDs required to consolidate UVP datasets (see section 2. 2. 2), and the information required for the standardization and quality control of these datasets. The dataset ID lists are generated automatically, but the data information (headers and units) are manually filled to map the native headers and units of the datafiles to standard names (following the variablename_field nomenclature) and units (following the variablename_unit nomenclature).
These spreadsheets can be downloaded at https://github. com/jessluo/PSSdb/tree/main/raw. The mapped variables include longitude, latitude, sampling time (with time format), minimum and maximum sampling depth, volume sampled and potential dilution factors, the lower and upper sample size limit, and optional additional metadata describing the sampling effort, protocol or downstream processing, the pixel size, and the ROI size estimates with taxonomic annotation. In the case of size-fractionated samples, the sampling size limits were determined by the mesh or filter sizes. Otherwise, the dimensions of the imaging frame are used to specify the theoretical upper size range imaged by the device. ROI size estimates may include biovolume, area, or ellipsoidal axis for comparison. However, the size spectra for PSSdb were all computed using ROI area for consistency across devices, since not all imaging instruments provide biovolume estimates and derived equivalent spherical diameter (see section 2.
2. 6 for more details). In addition, the value(s) for "Not Available" or NA were specified, if necessary, since we found some inconsistencies in the values reported, particularly for datasets generated by Zooprocess (i. e. UVP and scanner datasets), depending on the software version used, but also across variables for the same dataset. While the standardizer spreadsheet needs to be filled manually, we found this approach to be optimal to account for the variable formats of existing and future datasets, both accessible online or directly sent to us. Native units, defined in the standardizer spreadsheets, are converted to standard units using the python package Pint, designed to define, operate and manipulate physical quantities, based on units from the International System or defined in a custom text file.