dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2019) , to identify microbial species that are likely to have a causal role in colorectal cancer (CRC) risk and onset. Dynamic Bayesian networks (DBNs). Dynamic Bayesian Networks (DBNs) are BNs attested for modeling relationships over temporal data. In this regard, a DBN is a directed acyclic graph where, at each time slice or instance, nodes correspond to random variables of interest and directed edges correspond to their conditional dependencies in the graph  (Russell and Norvig, 2016). DNB has been used for analyzing longitudinal microbiome data sets to establish temporal relationships between different taxonomic ranks and other clinical factors that affect the microbiome  (Lugo-Martinez et al. , 2019). They studied longitudinal data sets from three human microbiome body sites: infant gut, vagina, and oral cavity, and use temporal alignments to normalize the differences in the progress of biological processes of each subject, they found that microbiome alignments improve the predictive performance of the methodology over previous studies of longitudinal datasets, and increase the ability to infer new and previously reported biological and environmental relationships between the components of the microbiome and other factors that influence it, this methodology allows to predict microbiome states and relationships based on longitudinal data applying DBN.
Moreover, authors build up the CGBayesNets package that is freely available under the MIT Open Source license agreement. In general, time series analyses represent a valuable approach to determine the resilience and variability of microbial communities. Perturbations and changing environmental conditions can drive communities into alternative stable states, while bi-and multi-stable states are mostly induced by member interactions within a microbial community. However, a detailed exploration of these temporal shifts is often restricted by either intensively sampled but small treatment groups or large studies, including only few sampling time points. Faust et al. (2015)  compared twelve-time series analysis techniques used for highthroughput sequencing studies. These techniques mostly operate on cross-correlation, autocorrelation or network inference.
, 2018). Shallow shotgun metagenome sequencing. Investigate the predictive ability of gut microbial markers in conjunction with conventional risk factors, for incident liver disease and alcoholic liver disease. Gradient boosting. Liu et al. , 2020  Serum lipids Healthy Finnish adults (n = 25, 18 females, 7 males). 16S rRNA gene data. Evaluate the association between the gut microbiome and lipid profile. Linear models, unsupervised hierarchical clustering. 2 ). The application of DP to human microbiome analysis is not well captured by our dataset as its application for human microbiome analysis is an emerging field. Recent example includes disease state prediction (inflammatory bowel disease, type 2 diabetes, liver cirrhosis, obesity) using deep representation learning framework that deploys various autoencoders to learn robust low-dimensional representations from high-dimensional microbiome profiles and trains classification models based on the learned representation  (Oh and Zhang, 2020)  or that relate key microbial biomarkers with metabolite biomarkers in gut microbiome  (Le et al.
, 2020). Our results indicate that the biomedical application of ML for analyses of human microbiome datasets has been mainly focused on the characterization of differently abundant microbial groups between different body sites and the effect of diet on microbiome composition and dynamics. The gut microbiome datasets have been extensively used to stratify and classify patients according to symptoms or characteristics to assist in the diagnosis End-to-end AutoML tool designed to deliver predictive and diagnostic models to non-experts while drastically increasing the productivity of expert analysts. Several qualifications make JADbio (www. jadbio. com) very suitable for microbiome data analysis. First, it accepts numerical measurements (e. g. , abundance tables), as well as discrete predictors (e.
g. experimental factors and curated metadata), and incomplete datasets with missing values. Second, it facilitates a novel out-of-sample bootstrapping protocol able to provide accurate, non-optimistic estimates of predictive performance even in cases of low sample sizes (e. g. , 40) and hundreds of thousands of features Finally. It uses SES and FBED to return the corresponding biosignatures. This allows the creation of predictive models that are equally good up to statistical equivalence, thus, providing the researcher with choices when designing new cost-benefit diagnostic assays. Tsamardinos et al. , 2018 Tsamardinos et al. , , 2020  Microbiome network inference with SCENERY. SCENERY is a free online application that allows users to perform several network learning tasks (scenery. csd. uoc.
Problem type Problem description Not cross-validating the feature selection step Perhaps the most common pitfall of performance estimation is that of performing feature selection on the complete, labeled dataset (e. g. by differential expression) and subsequently cross-validating only the modeling algorithm on the same data  (Hastie et al. , 2009). The same account for any other step on the pipeline that peeks at the labels or the outcome to predict. In the case of large sample and balanced datasets the overestimation should be unnoticeable. On small sample or imbalanced datasets, however, overestimation can become quite significant  (Tsamardinos et al. , 2020). An analyst should cross-validate all steps of the analysis as atoms, including the preprocessing, imputation, feature selection, and modeling to obtain accurate estimates of performance.
Not correcting for winner's curse A second common error is reporting the cross-validation predictive performance of the winning algorithm or ML pipeline as the final performance estimate. For example, an analyst may try 1000 combinations of different algorithms for each step of the analysis with various values for their hyper-parameters and find that the winning combination has a cross-validated accuracy of 80%. This estimate is on average, overestimated because of the "winner's curse"  (Ioannidis, 2008). The overestimation due to the winner's curse is again large in small or imbalanced datasets. It is not uncommon to find 0. 7 AUC when the true one equals random guessing (0. 5 AUC) due to the winner's curse. Other estimation protocols need to be applied in these cases. The simplest solution is to withhold a separate test set to estimate the performance of the winning model; unfortunately, this technique loses samples to estimation and cannot be applied when samples are scarce.
Not stratifying the split to folds Another typical error occurs when randomly splitting the available samples, either for creating an external validation dataset, or to perform cross-validation, without accounting the class imbalance and sample dependency. The partitioning should be stratified, i. e. , the class distribution should be maintained in the folds. When the classes are imbalanced, sample stratification leads to improved performance estimations  (Tsamardinos et al. , 2015).
However, it should be noted that many of the reviewed papers are focused on the comparison of the performance of different ML methods, developing workflows or creating new ML approaches considering the technical aspects of ML related to the nature and complexity of the microbiome data, but without a clear biological or clinical question behind to solve. A detailed analysis of the dataset obtained showed that 20 of 89 papers used their own unique datasets, while the rest of publications made repetitive and intensive use of a limited number of datasets to develop ML solutions, like the Human Microbiome Project widely used for microbiome body composition studies. Besides, we identified 9 papers related to the development of ML methods for microbiome longitudinal analysis that are mainly based on the reuse of five datasets  (Caporaso et al.
Building prediction models for the analysis of microbiome or similar biological data often requires the design of an ML pipeline in which different algorithms for data preprocessing, imputation, feature selection, and modeling are combined along with their hyper-parameter values. The implementation of such a complex modeling strategy could be tedious and requires substantial human resources to optimize. Most importantly, however, this process is prone to serious methodological errors that lead to models whose training performance estimates are inflated (overestimated) and, thus, fail to generalize on external validation datasets. Some common pitfalls of ML application are listed in Table  4.
In addition, for training powerful ML methods with reliable results a large amount of data and a lot of computing resources are required. In general, ML methods introduced in this review are based on datasets with a limited number of cases and without other independent datasets what conditions their results and applicability. Therefore, from our review perspective future efforts in the field should be focused in (1) create standards (incl data pre-processing) for the development and deployment of ML techniques with an easy, transparent, and trustable interpretability for nonexperts taking in account the peculiarities of microbiome data; (2) increase the number and quality of human microbiome studies; (3) create efficient data structures and ML repositories following Findable, Accessible, Interoperable and Reusable (FAIR) principles and (4) build bridges between different disciplines, microbiology, biology, statistics, bioinformatics, engineering and others to increase interdisciplinary for innovative solutions.
(Seo et al. , 2017)ion-based network analysis and dimensionality reduction to assess the effect of probiotics consumption and found that probiotic intervention reduced the abundance of potential bacteria such as Citrobacter and Klebsiella spp. in the human gut microbial community. Moreover, they found that probiotic intervention may reduce the flatulence through downregulation of Methanobrevibacter spp. abundance(Seo et al. , 2017). Figure2summarizes reviewed papers based on the input data type and ML method type. The most dominant input data type in the case application of ML methods for human microbiome analysis has been 16S rRNA amplicon-based sequencing data either in the form of OTU or ASV tables while usage of shotgun metagenomes has increased during recent years. There are a small number of studies that have tested ML methods on both amplicon-based and shotgun datasets.
Introduction Geobiology explores how Earth's system has changed over the course of geologic history and how living organisms on this planet are impacted by or are indeed causing these changes. For decades, geologists, paleontologists, and geochemists have generated data to investigate these topics. Foundational efforts in sedimentary geochemistry utilized spreadsheets for data storage and analysis, suitable for several thousand samples, but not practical or scalable for larger, more complex datasets. As results have accumulated, researchers have increasingly gravitated towards larger compilations and statistical tools. New data frameworks have become necessary to handle larger sample sets and encourage more sophisticated or even standardized statistical analyses. In this paper we describe the Sedimentary Geochemistry and Paleoenvironments Project (SGP; (2) create a website where data can be retrieved in a flexible way; and (3) build a collaborative consortium where researchers are incentivized to contribute data by giving them priority access and the opportunity to work on exciting questions in group papers.
Finally, and more idealistically, the goal is to establish a culture of modern data management and data analysis in sedimentary geochemistry. Relative to many other fields, the main emphasis in our field has been on instrument measurement of sedimentary geochemical data rather than data analysis (compared to fields like ecology, for instance, where the post-experiment ANOVA (analysis of variance) is customary). Thus, the longer-term goal is to build a collaborative environment where geobiologists and geologists can work and learn together to assess changes in geochemical signatures through Earth history. With respect to the data product, SGP is focused on assembling a well-vetted and comprehensive dataset that is tractable to multivariate statistical analyses accounting for multiple geological and methodological biases.
Phase 1 of the project, which focuses on the Neoproterozoic and Paleozoic, has been completed. Future phases will capture a broader range of geologic time, data types, and geography. The database contains tens of thousands of unpublished data points provided by consortium members, as well as detailed metadata that go beyond what is contained in papers. In many cases, these represent measurements that are tangential to a given published study but still of high utility to database studies; these allow the community to address questions that would be impossible to answer solely with the published data. For instance, in order to use a proxy such as Mo/TOC (total organic carbon) ratios in mudrocks deposited under a euxinic water column, the full suite of trace metal, iron speciation, and total organic carbon data is needed.
Likewise, geospatial information is required to account for sampling biases, and many statistical learning approaches cannot accept, or have difficulty with, incomplete geological predictor variables. Ultimately, it is this complete data matrix that will allow for SGP's most insightful analyses. This paper serves as an introduction to SGP, the process by which our data products are created, a description of the Phase 1 data product and a citable reference for that product, a description of the SGP website and API (Application Programming Interface) for open access, and a statement of our future goals.
Why SGP? In recent years there has been a welcome trend in the broader geochemical community towards increased data accessibility, documentation of sample context, and sample curation, albeit with challenges still ahead  (Brantley et al. , 2020; Cutcher-Gershenfeld et al. , 2016; Planavsky et al. , 2020). First, progress has been made through journals and organizations adopting stringent data archiving rules and promoting adherence to FAIR principlesfindability, accessibility, interoperability, and reusability ("FAIR Play in Geoscience Data," 2019;  Wilkinson et al. , 2016). Second, several databases now house geochemical data at different scales and with different focuses (e. g. ,  Brantley et al. , 2020; Gard et al. , 2019; He et al. , 2019; Lehnert et al. , 2000). Among the largest and most active are projects such as EarthChem (earthchem.
org), the Geobiodiversity Database (geobiodiversity. com), Pangaea (https://www. pangaea. de), and the StabisoDB (https://cnidaria. nat. uni-erlangen. de/stabisodb/). The SGP database was built with the data structures and standards of these other projects in mind, in keeping with FAIR principles and with the hope that data can be easily shared in the future. Consistent with the stance taken by other organizations in the community (e. g. ,  Hanson, 2016) , we also strongly encourage all members to register their samples for an International Geo Sample Number (IGSN; i. e. , globally unique alphanumeric sample identifiers), which can be obtained from the System for Earth Sample Registration (www. geosamples. org). However, SGP is a domain-specific project that differs from other databases in the way the data are collected, the nature of the data collected, and the tailored way in which they are presented to our research community.
Specifically, SGP is focused on addressing how geochemical proxy records change through deep time. Central to these goals are the following: 1) Compilation of a large quantity (i. e. , millions of records) of sedimentary geochemical data spanning deep time. 2) Appropriate age models (with uncertainty), especially for Proterozoic/Archean samples. 3) Information on interpreted depositional environment and specific rock type. 4) Information necessary to gauge if samples are likely to preserve primary, environmental geochemical signals. 5) Detailed methodological information on how the data were generated. 6) An ability to download the data of interest flexibly and easily. Although some other databases contain sedimentary geochemical data, the vast majority of deep time data is not available from any single source, and samples are not readily associated with critical contextual data-such as age constraints and environmental data-necessary for the types of proxy-through-time and/or environmental studies typically conducted in historical geobiology.
The first priority for Phase 1 of SGP was to assemble or generate multi-proxy sedimentary geochemical data (carbon and sulfur abundances and isotopes, iron speciation, major and trace metal abundances, and trace metal isotopes, primarily from fine-grained siliciclastic rocks) from multiple regions worldwide for every Paleozoic Epoch and equivalent ~25 Myr Neoproterozoic time slices. In addition to data compilation, this has involved an effort by SGP members to generate new geochemical data from 'background' intervals in the Paleozoic (i. e. , not associated with events such as mass extinctions or significant climatic shifts). The first phase of data collection came to an end in 2019. At that point a copy of the database was vetted by SGP team members and then archived-the first data 'freeze' (following the best-practices approach used in medical consortia).
Database SGP utilizes a relational database implemented with the PostgreSQL database management system. A full database diagram and documentation are available at https://github. com/ufarrell/sgp_phase1 and a simplified diagram is shown in Fig. 2. The design was inspired by several existing data models in the geological and natural history museum communities. Tables for analytical geochemistry are from the British Geological Survey (BGS) geochemistry data model  (Watson et al. , 2014) , with minor modifications. Tables for geological, geographical and sample details are based on established museum collection management databases (Specify 6 https://www. specifysoftware. org/ and Arctos https://arctosdb. org/) in addition to the Observations Data Model 2 (ODM2,  Horsburgh et al. , 2016; Hsu et al.
, 2017) , an information model for Earth observations. The SGP database is centered on the sample table (Fig. 2 ). Samples are generally characterized by an individual rock sample and all resulting analyzed powders. The three key sections of the database linked to samples are (1) analytical results and associated methods, (2) geographical context, and (3) geological context. Dictionary tables (standardized lists of terms, also known as 'controlled vocabularies') are based on existing community vocabularies where possible (e. g. , from EarthChem, ODM2, Macrostrat, U. S. Geological Survey (USGS), and BGS). However, in many cases these vocabularies required additions, such as the inclusion of specific sedimentary geochemical experimental methods (e. g. , sequential iron extraction techniques  (Poulton & Canfield, 2005) ).
The BGS data model for analytical methods and geochemical results has been adopted almost without modification. We store analytical data in their submitted or published format, and do not standardize the results to any given unit. An analytical result may be empty (NULL) only if it is below or above detection limits, and those values are also stored if they are available. If the results are published, they are linked directly to a reference work on an individual basis so that a fine-level distinction can be made between published and related unpublished data from the same samples. Any geostandards that are analyzed alongside samples in a study are also recorded. In the SGP, we make every effort not to include the same result twice. However, replicates may legitimately be added if the same sample has undergone analysis for the same analyte more than once (this could include anything from true replicate analyses using the same methods in the same lab to analyses of the same sample by different research groups using different methods).
Data Collection The SGP welcomes contributions from any interested researchers. Specifically, contributing data automatically makes a researcher part of the SGP Collaborative Team, rather than one needing to 'join' SGP to contribute data. In the first consortium-building stage, potential collaborators were targeted if their work was particularly relevant to the Phase 1 goals, and additional researchers were recruited via SGP representation at multiple conferences. SGP collaborators are involved in providing details about their samples and providing published data tables and unpublished data from their own archives. In addition, some data have been collected from relevant published studies where the authors are not directly involved. In such cases, contextual information was coded by SGP team members using information provided in the paper.
Maximum and minimum age estimates can also be stored, and indeed, are critical for the type of re-weighted bootstrap analyses employed by many SGP Working Groups  (Mehra et al. , 2021). A subset of samples from two USGS databases have been integrated into the SGP database. The first of the databases used is the National Geochemical Database: Rock (USGS NGDB, U. S. Geological  Survey, 2008) , comprising data from USGS projects from the 1960s-1990s, largely from North America. The second is the Global Geochemical Database for Critical Metals in Black Shales project (USGS CMIBS,  Granitto et al. , 2017) , which includes predominantly Phanerozoic shale data from all continents. Data from both USGS databases lack much of the contextual information available for samples directly coded by the SGP team members (most specifically basin type, metamorphic grade, depositional environment, and detailed age justification) and there are a higher proportion of analytes with less detailed geochemical methodology.
Nevertheless, they represent large numbers of samples (74% of samples in Phase 1 are from USGS sources) with age, lithology, and geographic information that can be utilized for many types of analysis. In the case of USGS NGDB, only sedimentary samples were incorporated into SGP, and in the case of USGS CMIBS, we did not include samples with lithologies indicative of ore or studies where the authors were primarily concerned with mineral deposits or studying the effects of metamorphism on shales. An attempt was made to match USGS fields to SGP fields, with some data cleaning needed in order to extract important information such as up-to-date stratigraphic names. Samples can easily be traced back to the original USGS databases using their original identifiers. The USGS NGDB data were enhanced by adding interpreted ages.
Samples were matched, using a combination of stratigraphy and location, to the continuous time age model in Macrostrat  (Peters et al. , 2018). Specifically, the minimum and maximum age estimates from the Macrostrat model were entered, and the interpreted age was entered as the average of these values. Only samples with matched interpreted ages were included from USGS NGDB. The USGS CMIBS samples were associated with Macrostrat continuous-time age models where possible and given age information by SGP team members where not. However, a proportion (36%) remain without ages, and filling those in is a key goal for Phase 2. These three sources of data (direct entry by SGP team members (26% of samples), the CMIBS compilation (16% of samples), and the USGS NGDB (58% of samples)) provide a robust base platform for statistical analyses of aggregated sedimentary geochemical data through Earth history.