dataesr/scientific-paragraphs-categorization huggingface dataset:
Viable (Solution outlined; Solution possible within constraints; Risks acceptable & manageable; Solution profitable; Reasons to develop solution understood; Pursuit viable), Addressed (Opportunity addressed; Solution worth deploying; Stakeholders satisfied), usage scenarios explained; Priorities clear; Impact understood; Team knows & agrees on what to deliver), Acceptable (Acceptable solution described; Change under control; Value to be realized clear; Clear how opportunity addressed; Testable), Addressed (Enough addressed to be acceptable; Requirements and system match; Value realized clear; System worth making operational), Fulfilled (Stakeholders accept requirements; No hindering requirements; Requirements fully satisfied) Software System: Architecture Selected (Architecture selection criteria agreed; HW platforms identified; Technologies selected; System boundary known; Decisions on system organization made; Buy, build, reuse decisions made; Key technical risks agreed), Demonstrable (Key architectural characteristics demonstrated; System exercised & performance measured; Critical HW configurations demonstrated; Critical interfaces demonstrated; Integration with environment demonstrated; Architecture accepted as fit-for-purpose), Usable (System can be operated; System functionality tested; System performance acceptable; Defect levels acceptable; System fully documented; Release content known; Added value clear), Ready (User documentation available; System accepted as fit-for-purpose; Stakeholders want the system; Operational support in place), Operational (System available for use; System live; Agreed service levels supported), Retired (Replaced or discontinued; No longer supported; No authorized users; Updates stopped) Team: Seeded (Mission defined; Constraints known and defined; Growth mechanisms in place; Composition defined; Responsibilities outlined Required commitment level clear; Required competencies identified; Size determined; Governance rules defined; Leadership model selected), Formed (Enough members recruited; Roles understood; How to work understood; Members introduced; BeeAPEX project the course recognized and awarded with 3 ECTS; an e-book on low-code programming with APEX, a short course for independent learning on low-code development of front-and back-end applications and database; a 5-day training for professors, lecturers and postdoctoral teaching assistants; pre-recorded webinars on industry and employment trends; case studies created jointly with local SMEs on low-code programming; academic articles; a project website with all relevant information about the project activities, outputs and results,; a showcase of selected students' project work; dissemination and multiplier activities; more inclusive and student-centered IT education.
databases: (1) the "European Health for All Database" is the oldest WHO database containing indicators: population health status, health determinants, health risk factors, and financial resource and cost factors health care;(2)  the Global Health Observatory database provides a basis for comparing the rates of death and loss of health due to disease and injury in all parts of the world. The data of this database were formed as individual member states submitted them to the WHO; and (3) the "Mortality Database" is a source of disparate mortality data, by age, sex, and cause of death. ECHI is a project of the European Commission (EC) and primarily observes different spheres of life of European residents, and the data is grouped according to several dividing criteria: urban/rural population, young/old.
ECHI classifies its indicators into groups: M. Lugonjić, O. Arsenijević, P. Šprajc: Health System Indicators -A Case Study of the Republic of Serbia 587 demographic and socio-economic indicators, indicators of population health status, indicators of factors influencing health status, indicators of health service quality and indicators of successful promotion of a healthier lifestyle. The ECHI database contains some data for Serbia taken from the WHO, but since its targeted research does not cover Serbia (but only members of the European Union), it has no added value for this research. • European Health Consumer Index (European Health Consumer Index -EHCI). The EHCI was developed under the auspices of the agency "Health Consumer Powerhouse Ltd" as an independent monitoring of the health systems of 35 European countries, the results of which are reported to the European Parliament today (https://healthpowerhouse.
• MEAN Stack (MongoDB, ExpressJS, AngularJS, Node. js): This stack is popular for building headless CMS systems. o MongoDB is the database management system, o ExpressJS is the web framework, o AngularJS is the front-end framework, and o Node. js is the JavaScript runtime. This stack is widely used for building and managing web and mobile applications, particularly those that require real-time data processing and handling. • Ruby on Rails Stack (Ruby, Rails, PostgreSQL): This stack is also popular for building traditional/monolithic CMS systems. o Ruby is the programming language, o Rails is the web framework and o PostgreSQL is a database management system. This stack is widely used for building web applications, particularly those that require high scalability and performance. •. NET Stack (C#, ASP.
NET, SQL Server): This stack is popular for building traditional/monolithic CMS systems. o C# is the programming language, o ASP. NET is the web framework and o SQL Server is the database management system. This stack is widely used for building enterprise level web applications and sites that require high performance and scalability. • Django Stack (Python, Django, PostgreSQL): This stack is also popular for building traditional/monolithic CMS systems. o Python is the programming language, o Django is the web framework and o PostgreSQL is a database management system.
Broad Application Domains and Business Needs -Reuse Crucial. Oracle APEX (Application Express) is a LCP for Web apps based on an Oracle Database Management System (DBMS) 2 Oracle APEX from a Bird Eyes View Paper Contribution and Structure. Based on the findings in BeeAPEX, the overall contribution herein is a primer for getting started with LCPs from a non-IT-expert perspective. Focusing on Oracle APEX as representative example, observations are generalizable across DB-centric LCPs (Bock et al. , 2021)   as we (i) put forward a W.
In phylogenomics, incongruences between gene trees, resulting from both artifactual and biological reasons, are known to decrease the signal-to-noise ratio and complicate species tree inference. The amount of data handled today in classical phylogenomic analyses precludes manual error detection and removal. However, a simple and efficient way to automate the identification of outlier sequences is still missing. Here, we present PhylteR, a method that allows a rapid and accurate detection of outlier sequences in phylogenomic datasets, i. e. species from individual gene trees that do not follow the general trend. PhylteR relies on DISTATIS, an extension of multidimensional scaling to 3 dimensions to compare multiple distance matrices at once. In PhylteR, distance matrices obtained either directly from multiple sequence alignments or extracted from individual gene phylogenies represent evolutionary distances between species according to each gene.
Here we present PhylteR, a new method that can accurately and rapidly identify outliers in phylogenomics datasets. Unlike Phylo-MCOA  (de Vienne et al. 2012) , from which it is largely 2 inspired, it is an iterative process where obvious outliers are removed first, leaving space for better identification of more subtle ones, and leading in fine to a finer identification of outliers. Unlike TreeShrink  (Mai and Mirarab 2018) , it is not based solely on the diameter of unrooted gene trees and is thus more accurate when outliers are not associated with long branches (e. g. topological incongruences). Also, PhylteR relies on the multivariate analysis method DISTATIS  (Abdi et al. 2005; Abdi et al. 2012) , which is specifically designed, unlike Multiple Co-inertia Analysis (MCOA,  Chessel and Hanafi 1996)  used in Phylo-MCOA  (de Vienne et al.
2012) , to compare distance matrices, and is thus more appropriate for the problem at hand. We tested PhylteR on two types of datasets: a collection of small and simple simulated datasets where outliers were known, and a biological dataset comprising 14,463 genes for up to 53 species previously used for Carnivora phylogenomics  (Allio et al. 2021). In this empirical dataset, outliers were of course unknown but "properties" associated to gene sequences can be gathered (see  Shen et al. 2016  for a list of such properties). After illustrating the principle of PhylteR on the simulated datasets, we focused on the Carnivora gene sets: we characterised the sequences that were filtered out by PhylteR and we looked at the effect of PhylteR on the overall concordance between the gene trees and the species tree after filtering.
We compared the results with those obtained with TreeShrink  (Mai and Mirarab 2018) , the only other tool to our knowledge with a similar objective that could reasonably be applied on such a large dataset. We show that PhylteR identifies outlier sequences with more precision and sensitivity than TreeShrink in most cases. For instance, only PhylteR correctly identifies species in gene trees whose phylogenetic placement is not in accordance with its placement in other gene trees, which can result from biological processes such as horizontal gene transfers or hidden paralogy. We also provide strong evidence that the automatic removal of outliers with PhylteR improves the concordance between gene trees and the species tree in greater proportions than TreeShrink  (Mai and Mirarab 2018). We hope that PhylteR could become the standard that was lacking  (Philippe et al.
Datasets We used two types of datasets to evaluate PhylteR and compare it with TreeShrink: a collection of simulated simple examples, and a large Carnivora phylogenomic dataset with 53 species  (Allio et al. 2021 ). These datasets are described below. • Simulated datasets (SD1 to SD4): We generated a collection of simple datasets in order to either illustrate the different steps of the PhylteR process (SD1), or to compare PhylteR and TreeShrink in terms of their ability to detect misplaced species in gene trees (SD2), long-branch outliers in gene trees (SD3) or both types of outliers when mixed (SD4). These simulated datasets were obtained with the simtrees() function in the R package phylter (this publication). The way this function works is as follows: a single phylogenetic tree with a given number of species (Nsp) is randomly generated with function rtree() from package ape v5.
6. 2  (Paradis and Schliep 2019). This tree is duplicated as many times as the number of orthologous gene families required (Ngn). • Carnivora dataset (CD): We used the raw sequence files (before alignment and filtering) from a previously assembled phylogenomic dataset comprising 14,463 genes for 53 species aimed at resolving the phylogeny of the order Carnivora  (Allio et al. 2021). This dataset was obtained by extracting single-copy protein-coding orthologous genes from the genomes of 52 carnivore species, plus the Malayan pangolin (Manis javanica) used as outgroup, following the orthology delineation strategy of the OrthoMaM database  (Scornavacca et al. 2019 ). These raw sequence files were aligned and filtered using the OMM_MACSE pipeline  (Ranwez et al. 2021).
Evaluation of the accuracy of PhylteR outlier detection and comparison with TreeShrink We evaluated PhylteR's ability to detect outliers that are either correct (when it is possible to test it, with simulated datasets) or meaningful according to the biological information we can gather from the dataset at hand. We used the first simulated dataset (SD1) for illustration purposes only. For the simulated datasets SD2 to SD4 (Table  1 ), and for each one of the 20 replicates, we ran PhylteR with default parameters and we counted the number of True Positives (TP, outlier sequences that were simulated and that are retrieved), False Positives (FP, outlier sequences that were not simulated but are identified) and False Negative (FN, outlier sequences that were simulated but are not retrieved). From those, we computed the mean precision (TP/(TP+FP)) and recall (or sensitivity, TP/(TP+FN)) of the outlier identification of PhylteR.
For comparison purposes, we performed the same analyses using TreeShrink v1. 3. 9  (Mai and Mirarab 2018)  in place of PhylteR with default parameters for detecting outliers. For the Carnivora dataset, we have no access to the true outliers. It is thus impossible to compute precision and recall on this empirical dataset as done on the simulated ones. Instead, we can compute "features" associated to each gene sequence for each species (sequence hereafter), that are, a priori, associated with errors or with lack of signal in phylogenomic datasets. We can then evaluate whether the outliers detected by PhylteR are enriched in extreme values for these features, as compared with randomly selected sequences or with outliers identified with TreeShrink. The list of features and the reason for their choice is listed below.
• Sequence length: Long sequences were shown to carry more phylogenetic signal than shorter ones  (Salichos and Rokas 2013; Shen et al. 2016). To explore the possible enrichment of outliers in short sequences, we computed the length (in bp) of each sequence in each gene MSA, and explored its distribution in outliers. • Duplication score: when a sequence in a gene tree is not orthologous to the others but is a paralog, its localization in the gene tree is likely to be incorrect. To have an insight into the level of "paralogousness" of each sequence in the Carnivora dataset, we compared the Carnivora species tree published in  Allio et al. (2021)  with each one of the 14,463 gene trees using the reconciliation program ALEml_undated  (Szöllősi et al. 2015). This tool allows inferring the duplications, losses and transfers experienced by a gene by comparing its history (the gene tree) with that of the species (the species tree).
If an outlier detection method is indeed able to remove hidden paralogs, we should see an enrichment of KZNF genes in the list of outliers. • Synteny: Synteny (in our sense) is the link between two genes occurring consecutively on a genome, i. e. without any other gene (in the dataset) located between them. One gene then has two synteny linkages. A synteny break occurs when two genes are consecutive in one species but their orthologs in another species are not. The direction of transcription (coding strand) is considered, i. e. if it has changed it is considered as a break even if the genes appear in the same order. One gene, compared to its ortholog in another species, may then be associated with 0, 1 or 2 breaks. We call genes associated with 2 breaks syntenic outliers. Synteny breaks are due to genomic rearrangements (inversions, duplications, translocations, …), but can occur in the data, and in much larger proportion, for many artifactual reasons: annotation errors, assembly errors, or orthology assessment errors.
Evaluation of the impact of outlier sequences removal on species tree support It is expected that a tool that accurately removes outliers in phylogenomic datasets should increase the concordance between the gene trees and the species tree. To evaluate this and compare PhylteR with randomly sampled sequences and with TreeShrink-identified outliers, we computed the gene concordance factor (gCF,  Minh, Hahn, et al. 2020)  as implemented in IQ-TREE version 2. 1. 3 (Minh, Schmidt, et al. 2020)  for every branch in the Carnivora species tree (obtained from  Allio et al. 2021). For each branch of the species tree, this factor indicates the percentage of gene trees in which this branch is found (among gene trees where this can be computed, or "decisive" trees, see  Minh, Hahn, et al. 2020). gCF was computed according to either the original gene trees (gCF init ), or to a list of gene trees obtained after pruning outliers (four sets of gene trees corresponding to the four list of outliers in Table  2 ).
Illustration of the general principle of PhylteR The different steps of the PhylteR process (Figure  1 ) are illustrated on a simple example dataset comprising 25 genes for 20 species, with 10 outliers (Table  1 ). The main steps are as follows. Individual gene trees are transformed into individual gene matrices that are then combined into a unique compromise matrix obtained after weighting each matrix by its concordance with the others: matrices that are poorly correlated with the others have less weight in the creation of the compromise (Figure  S1A-E ). This matrix is then projected onto a space on which individual matrices are projected as well  (Figure 2A and S1F). By computing the distance of each species in each orthologous gene to its reference position in this projection, the two-way reference matrix is obtained  (Figure 2B and S1G).
PhylteR performs well on simple examples with all types of outliers To evaluate the precision and sensitivity of PhylteR, we used it on three simplistic datasets (SD2, SD3 and SD4, table  1 ). We also computed precision and recall on the same datasets using another method, TreeShrink  (Mai and Mirarab 2018). The SD2 simulated dataset contained only outliers of type "topology", i. e. outliers obtained by moving some species to another location in some gene trees. For this type of outliers, PhylteR performs very well, precision and recall being very close to their maximum value 1 (Figure  3 , left). On the other hand, TreeShrink performs very badly, usually identifying no correct outliers at all (leading to a mean precision close to 0), but still detecting a large collection of false positives (leading to a low sensitivity).
When outliers are obtained by increasing branch lengths for some species in some genes (the SD3 datasets), PhylteR is still performing very well in terms of precision (mean = 1, Figure  3 , middle), and better than TreeShrink, mainly because TreeShrink detects many false outliers. In terms of recall now, PhylteR appears slightly lower than TreeShrink. Part of this lower performance, however, can be due to the fact that TreeShrink detects many more outliers than PhylteR. Another reason is that TreeShrink is specifically designed to detect outliers with long branches and that for this specific task, despite many false positives, it seems more sensitive than PhylteR. Finally, when both types of outliers are mixed in a dataset (SD4), PhylteR outperforms TreeShrink (Figure  3 , right). This is easily explained by the results of the tests on the two previous datasets.
Characterisation of outliers detected with PhylteR on the Carnivora dataset Outliers in phylogenomic datasets can be of different nature: fast or slow evolving genes in some species, leading to respectively long or short branches in gene trees, or species being placed in aberrant position in some genes because of incomplete lineage sorting (ILS), horizontal gene transfers (HGT), hidden paralogy, saturated signal, compositional bias, long-branch attraction, or other artifactual reasons (Schrempf and Szöllősi 2020). In the set of 14,463 gene trees analysed by PhylteR, two sets of outliers (7,183 and 20,157 sequences) were identified with PhylteR (with default or tuned parameters, respectively) and 7,032 and 19,643 with TreeShrink (with tuned and default parameters respectively, see Table  2 ).
2e-16 for all comparisons). This result is in accordance with the results obtained on simulated datasets: PhylteR is good (and much better than TreeShrink) at identifying misplaced species in some gene trees, which is indirectly what the duplication score captures. One illustration of the difference between PhylteR and TreeShrink in their ability to capture duplicated sequences (and thus probably hidden paralogous) can be given by the study of peculiar proteins, such as the Zinc-finger family (ZNF). This large family of paralogs first duplicated from the gene PRDM9 or PRDM7 in the ancestor of vertebrates  (Emerson and Thomas 2009). These genes are involved in the repression of transposable elements and are still actively duplicating. The high number of duplications renders the resolution of the orthology relationship in this gene super-family very challenging.
In the Carnivora dataset, the ZNF super-family has been splitted in 168 orthologous gene families  (Allio et al. 2021). As expected in case of hidden paralogy, we see an overrepresentation of the genes belonging to these families in the list of outliers, especially in the outliers identified by PhylteR (Figure  5C ). Between 3. 79% (for the large set) and 7. 4% (for the small set) of PhylteR outliers belong to the ZNF family, while these values drop to 1. 78% and 1. 12% respectively for TreeShrink outliers, and less than 1% for randomly selected sequences (Figure  5C ). 18 Third, we compared two by two 14 Carnivora species and identified syntenic outliers (see material and methods). In almost all pairwise comparisons, we found that these syntenic outliers 19 significantly overlap the outlier sequences detected by Phylter.
For example, in the comparison between Zalophus californianus and Suricata suricatta (illustrated in Figure  6 ), out of the 5,123 genes common to both species in the dataset, 131 (2. 56%) are syntenic outliers (i. e. surrounded by two breaks). In comparison, out of the 47 outlier sequences identified by PhylteR (small list) in either Zalophus californianus or Suricata suricatta, 38 are syntenic outliers (80. 8%), which is   S2  and S3 ). species give similar results). On each circle, a reference Zalophus scaffold is represented in dark blue, and all scaffolds for which at least one gene has an ortholog in this scaffold are in light grey. Lines between these scaffolds represent couples of genes annotated as orthologous. Red lines highlight gene outliers detected in Suricata suricatta. We observe that they are very often "isolated" genes, i.
Here, with PhylteR, we propose a way of analysing large collections of gene trees by using an automatic method that can simultaneously analyse a large collection of distance matrices (retrieved from gene trees or directly from MSA), identify the common signal between these matrices, and identify elements (outliers) in some of these matrices that are responsible for a decrease in concordance. By using a process where these outliers are automatically and iteratively removed, we propose a new way of efficiently identifying them. Evaluating a method for its capacity to accurately identify errors in phylogenomics datasets is a difficult task. As for any inference method, we use simulations. However, simulating the processes that result in errors (in our case, outliers in phylogenomics data) has no standard solution: sources of errors are numerous, they combine with each other through all phylogenomic pipelines, sometimes with unpredictable results.
Horizontal gene transfers (HGT) or Incomplete Lineage Sorting (ILS), for instance, may lead to high duplication scores according to our approach when none occurred (even though HGT is thought to be anecdotal in the carnivora dataset). Similarly, artefactual reasons such as long branch attraction, annotation error or alignment error can lead to misplacements of species in some gene trees. A more direct way of testing the ability of PhylteR to detect hidden paralogous sequences was to focus on a specific gene family known to be extremely diverse because of multiple duplication events, the KZNF family  (Huntley et al. 2006; Liu et al. 2014). We observed a clear enrichment of sequences belonging to this peculiar family in the list of outlier sequences identified by PhylteR, as compared to those identified by TreeShrink or randomly sampled.