dataesr/scientific-paragraphs-categorization huggingface dataset:
Boruta Feature Ranking and Multicollinearity Check The comparative importance of the flood-influencing factors was visualized before the model's validation. This step can help in determining the model's ability to improve prediction accuracy to cope with flood hazard zoning. The efficiency of each factor was measured using the statistical properties of its correlation with flooding and classification status. The Boruta feature ranking technique was used to identify the most significant factors for the forecast of FHZ  [14]. Here, the randomness was given to the data set by creating shuffled copies of all features (shadow data set). Then, the iteration process checked whether a real feature has a higher importance than the best of its shadow features. The iteration continued until all the features were either confirmed or rejected.
Machine Learning Model 2. 10. 1. Random Forest The random forest (RF) model is an ensemble tree-based machine learning model coined by Breiman  [45]. The goal of an ensemble learning technique is to combine the training of various decision trees into a single prediction. RF can also be used to estimate the accuracy of various decision tree classifiers on different subsample points (bootstrapping) when building trees (ntree). It uses simple averaging to improve its accuracy and control overfitting while maintaining the subsample size. In the RF technique, if the bootstrap criterion is True, then the whole dataset is used to build the tree.
This paper presents the outputs of a project to improve student learning in complex geological environments using VR. Here we outline a workflow that was developed to collect high resolution imagery using remote sensing to create digital outcrop models (DOM) of complex geological sites. Using this framework, this paper will then explore the use of VR for an investigation of the Husavik Triple Junction, a complex structural site in northern Iceland, explaining how the drone data was converted to a 3D DOM and demonstrating how VR can be used to simulate real world field mapping. Finally, we describe how these IVR activities have been integrated into taught modules at postgraduate level and discuss how the use of IVR experiences can complement existing geoscience curriculum design.
The case study presented here focuses on a DOM of a unique subaerial rift-transform triple junction in the north east of Iceland. Developed from high-resolution drone imagery collected in 2016, this DOM presents a complex tectonic region that holds a great deal of potential for geoscience learning in a remote and unique geologic locale that students would otherwise likely never have the opportunity to visit. Using GeaVR (https://geavr. eu), a VR software package developed by 3DTeLC and Argo3D, we present a method for generating 3D environments, explore current capabilities of the software and focus on the potential applications in teaching geosciences using a complex structural site such as this. We will draw on first-hand experience of using VR technologies and our development of course content using these models, discussing the benefits to geoscience learning a site like this provides and addressing the limitations of the technology and the barriers to fully implementing IVR as a part of the geoscience curriculum.
Field data mapping and measurement The original field mapping was undertaken to address the need for very detailed mapping of small-scale tectonic structures; this involved a field survey coupled with detailed GPS referenced ground measurements and field surveys with mapping at approximately 1:200 scale using imagery obtained from low-altitude GPS-controlled drone surveys. Field data collection involved a small team working systematically across the mapping area; piercing points were identified and measurements of slip direction and displacement amount were recorded. Each piercing point was carefully selected, so that only those with a clear match on either side of the fracture opening were used to measure the opening direction and opening amount. A handheld GPS was used to locate each piercing point location, and a measuring tape and compass were used to measure the offset (opening direction and amount).
This field data collection was supplemented by a contemporaneous survey using an. Unmanned Aerial Vehicle using a Structure from Motion (UAV-SfM) data processing framework. Aerial imagery was acquired using a small multi-rotor unmanned aerial vehicle which was then post-processed to generate an ortho-rectified mosaiced colour image and Digital Surface Model of ground elevation (DSM). The drone used was a DJI Phantom 3 quadcopter fitted with onboard autopilot and GPS receiver, capable of automatic mission planning. A drone mounted camera captured vertical overlapping photographs of the ground surface at a resolution of 12 megapixels. GPS ground control points were installed across the study area using white markers to provide GPS calibration for post processing of the photography. The imagery acquired from the drone survey provided very high-resolution georeferenced image mosaic that was visualised in GIS to help extend field observations beyond visible line of sight.
The resulting individual photographs and GPS information were post-processed using the commercial software package Agisoft PhotoScan version 1. 4 to create a colour ortho-mosaiced image and DSM of the survey area by stitching the individual photographs. The image ortho-mosaic generated from the drone survey is shown in Fig. 3  and a comparison of the ortho-mosaic generated from the drone survey and the same area in Google Earth imagery is shown in Fig. 4. The full extent of the technical background of this approach is not detailed here, but the use of UAV-SfM for geological and geomorphological applications has been described by  Bemis et al. (2014) ,  Carrivick et al. (2016)  and  Smith et al. (2016).
These scene files are loaded using the GeaVR viewing tool. Both the converter and viewer tools were created as part of the Erasmus+ funded project 2017-1-UK01-KA203-036719 and the MIUR project ACPR15T4_00098-Argo3D (http: //argo3d. unimib. it/). The GeaVR software used in the study is available at https://geavr. eu and all learning resources are available to download from http://www. 3dtelc. com. For an example application and description of the workflow using these tools, see  Krokos et al. (2019) ,  Tibaldi et al. (2020)  and  Antoniou et al. (2020).
All outputs can be exported for use outside of the virtual environment, for example, the points, lines or polygon mapping can later be exported for use in GIS, either in KML or CSV format and the profile data can be exported as CSV data for viewing and plotting in Microsoft. The DOM of the Husavik Triple formed the basis for development of virtual teaching and learning activities as a deliverable from the Erasmus funded project (Erasmus+ 3DTeLC Project Summary, 2021). This model has been used for structural and geomorphological mapping using VR, replicating a walkover survey of the site on foot, whilst also taking advantage of the birds eye view afforded by the drone flight mode.
It is then apparent that the added distortion introduced in the DOM and VR would further degrade the accuracy of any data collected. From a research point of view these limitations can affect the quality and reliability of any meaningful data collection using the DOM and are important to consider, especially for researchers intending to study these sites further in VR. However, with regard to using the Husavik DOM as a teaching tool, this does not detract from benefits previously discussed. The ability to view this complex site in VR is an immersive experience, allowing the user to clearly identify important geological structures across the site and affording the user a greater understanding of the spatial scale of these structures. This has the potential to resolve conceptual difficulties that are often encountered by students during field experiences, such as interpreting 3D structures from 2D imagery, and affords a greater understanding of the geologic relationships on display at this site  (Whitmeyer et al.
In the case of our Husavik teaching materials the user is tasked with building a complete structural model of the site using measurements collected in VR and preparing this data as a figure to be presented using GIS software. Sessions using VR must be carefully planned with clear sight of how the activities align with the learning outcomes for the module. VR should only be included if there is a reasonable expectation it could enhance student learning and engagement  (Detyna and Kadiri. , 2019; Stojšić et al. , 2017). Use of VR for virtual field activities requires significant preparation, including allowing time for students to become familiar with VR and providing clear instructions that set out the aims and objectives of the session. Students have shown that without specific guidance virtual experiences can prove daunting  (Dolphin et al.
Learning materials This teaching plan described for the Husavik site has been replicated for DOM's of geomorphological sites for other hazard landscapes, including landslides (Black Ven & Chale, UK), lava flows (Mt Etna) and coastal spits (East Head, UK). The outputs from the Erasmus+ funded project are available to download from the EU project portal (Erasmus+ 3DTeLC Project Summary, 2021) and http://www. 3dtelc. com, while the GeaVR software is available at https://geavr. eu. These resources include the drone-to-VR scene converter and VR viewing software, raw drone imagery, Oculus VR ready scene files created from the drone imagery and teaching materials describing learning activities for each of the virtual reality sites (including the Husavik Triple Junction site discussed herein). For each of the chosen geological sites, for which drone imagery was acquired and DOM's created, detailed learning activities were devised.
, 2021; Paz-Álvarez et al. , 2022; Bursztyn et al. , 2022). Advantages of using VR as a part of existing fieldwork plans include their use as part of pre-fieldwork site orientation, induction and health and safety overview; and post-fieldwork debriefing and subsequent formative and summative feedback. While interest in educational VR applications remains high  (Bonali et al. , 2021) , they are often underutilised and research thoroughly describing how VR based teaching can be adopted into the curriculum is uncommon  (Radianti et al. , 2020; Klippel et al. , 2019). This Erasmus+ funded project aimed to tackle this shortcoming by developing the software and workflows required to generate VR models from raw drone imagery, establish a database of VR models for different geological settings and develop a range of teaching materials, videos and learning activities to compliment these VR models that are aligned to learning outcomes of standard undergraduate and postgraduate geoscience modules.
There are significant advantages to teaching using IVR and sites that are impractical for student visitation become alive in a virtual space that traditional 2D teaching cannot replicate without field work. 3D learning provides enhanced learning outcomes over similar methods currently delivered in 2D, helping students contextualise space and further develops spatial skills  (Bond and Cawood. , 2021). Though it is unlikely that students would accept VR as a replacement for real world experiences, existing fieldwork preparation and case studies can be enhanced using VR if implemented in a way that complements the existing curriculum, either utilising the 3DTeLC database of field sites, converting existing orthomosaics or through the development of new models. Following the workflow presented in the case study, DOM's can be generated that can help develop teaching materials for future student year groups.
, 2021) , but existing fieldwork preparation and case studies can be enhanced using IVR if implemented in a way that complements the existing curriculum, either by utilising an existing database of DOM's, or by converting DOM's for use in VR. The Husavik Triple Junction DOM presented in this study shows that while the quality of these 3D models are currently not sufficient enough for researchers on a site of this scale for thorough data collection, their use as an educational tool, especially when paired with VR to create an immersive experience, has an important role to play on existing geoscience curricula. Implementation of IVR activities based on DOM's of geological sites, replicating traditional fieldwork techniques, requires careful planning and should include pre-sessional orientation and induction, while allowing time for students to become familiar with VR hardware and software  (Wright,.
ABSTRACT The Orthology Benchmark Service (https: //orthology. benchmarkservice. org) is the gold standard for orthology inference evaluation, supported and maintained by the Quest for Orthologs consortium. It is an essential resource to compare existing and new methods of orthology inference (the bedrock for many comparative genomics and phylogenetic analysis) over a standard dataset and through common procedures. The Quest for Orthologs Consortium is dedicated to maintaining the resource up to date, through regular updates of the Reference Proteomes and increasingly accessible data through the OpenEBench platform. For this update, we have added a new benchmark based.
The Quest for Orthologs (QfO) consortium is a community effort, bringing together orthology inference software and database developers and their users  (4) (5) (6) (7) (8). The consortium has been active for >10 years  (9) , and has continually sought to define common standards shared by diverse stakeholders in the scientific community. One of its most widely recognized efforts is a public benchmarking platform that reports on the performance of several orthology inference methods using various metrics  (10). The benchmarking platform (available at https: //orthology. benchmarkservice. org/) has evolved over time to reflect the ever-changing landscape of publiclyavailable orthology inference methods. Specifically, we aim to make use of incremental improvements in reference genomic datasets and leverage curated datasets that may be used as a proxy for ground truth  (10, 11).
NEW QFO REFERENCE PROTEOMES (2020 DATASET) For standardized and fair benchmarking, and for the results of individual inference methods to be comparable, the benchmarking service needs a consensus dataset of proteomes (hereby understood as the collection of canonical protein sequences from every protein-coding gene in a species). The QfO Reference Proteomes (https://www. ebi. ac. uk/reference proteomes/) have been jointly designed for this task by the QfO community and the UniProtKB database  (12) , with a focus on including well-annotated species of medical and scientific interest, and on broadly covering the Tree of Life while staying of manageable size for every orthology inference provider. The dataset is updated annually; the latest version (2020) comprises 78 species (48 Eukaryotes, 23 Bacteria and 7 Archaea) based on the UniProtKB 2020 04 release (apart from the Xenopus tropicalis (UP000008143) reference proteome from 2020 06 release data).
In aggregate, this represents 1 403 509 protein sequences (984 137 canonical protein sequences and 419 372 isoforms)--a ∼7% increase from the 2018 QfO Reference Proteomes dataset (1 311 679 total sequences). Improvements to the 2020 version of the QfO Reference Proteomes are largely due to updated genome assemblies (Supplementary Table  S1 ), improved genome annotation at the source databases (e. g, Ensembl and RefSeq) and manual curation of entries in UniProt. The QfO Reference Proteomes are available for download in various formats: the protein sequences as FASTA and SeqXML files, CDS sequences for most proteins as FASTA files, and, for an increasing number of species, genomic locus coordinates are available in the XML format. Reference proteome datasets are generated using a genecentric approach which identifies all protein isoforms for a gene and selects the canonical protein sequence as representative of the set.
The generation of these datasets requires a synchronized update effort of the underlying databases that are the source of protein sequences and gene annotations (including the European Nucleotide Archive, Ensembl, Ref-Seq, and Model Organism Databases). Generating the 2020 dataset posed a challenge, as the Xenopus tropicalis annotation was out of sync across these databases. This was updated and the latest dataset was provided as part of the 2020 06 UniProt release and QfO 2020 release. The new QfO 2021 release has been updated to the new X. tropicalis genome assembly (v10). To help identify the changes in reference proteome sequences, we provide a new STATS file, including a summary of changes to the number of records in the canonical FASTA, additional FASTA and gene symbol to UniProt accession (gene2acc) mapping files, along with report of changes to the source genome assembly for a proteome.
Expectedly, the results for previously available methods recapitulate the key findings of previous years: BBH  (13)  and RSD  (14) , two naive methods for inferring one-to-one orthologs, were generally outperformed by the publicly available orthology inference algorithms, and no methods consistently outperformed the others across the benchmark. The benchmarking services have settled on a two years rotation to update its public results, in order to allow time for the main orthology resources and software to provide orthology predictions on each new proteome set, which can consume a lot of computing time. The latest public results are available for the 2020 proteome database on the benchmarking service website (https: //orthology. benchmarkservice. org/proxy/projects/2020/). It covers 20 orthology prediction sets coming from 11 different providers -one single resource can provide different types of orthologs, or a software can be run with multiple settings.
As was noted in previous years, there is not a clear binary distinction in performance between graph-based and treebased methods. Still, most graph-based methods that primarily aim to provide pairwise orthology inference (including one-to-many, many-to-many and paralogous relationships) tend to rank similarly and cluster together, generally close to the Pareto frontier at the balance between precision and recall. This is also the case of MetaPhOrs (  19 ), a metapredictor based on multiple gene tree databases that also provide mainly orthologous and paralogous relationships, and PhylomeDB (20), a tree-based method using the species overlap  (21)  algorithm to reconstruct orthologous relations. PANTHER  (22)  and OrthoFinder  (23) , both methods that ultimately aim to provide gene trees, also rank within this 'balanced' cluster, though generally with a slight bias toward higher recall (and lower precision).
In total, the dataset covers 18,282 sets of orthologous genes from eight mammalian species (Homo sapiens, Pan troglodytes, Macaca mulatta, Bos taurus, Canis lupus familiaris, Felis catus, Equus caballus and Sus scrofa), the first four of which are included in the QFO dataset and can be used for benchmarking. (3)  and detailed on the Benchmarking Service: https://orthology. benchmarkservice. org/proxy/projects/2020/) and provides one or multiple kinds of orthologous prediction. One-to-one orthologous pairs (orthologous relationships between single genes), co-orthologous pairs (Pairs of orthologs between species, may involve one or multiple genes in the same species depending on duplication), Orthologous clusters (a group of orthologs or paralogs defined at one taxonomic level and computed by a graph clustering method), Hierarchical orthologous groups (nested groups of orthologs at different taxonomic levels) and Gene Trees.
The result is shown in Figure  1. As expected, considering that the dataset concerns orthology relations between mammalian species, most methods perform well on the precision front, with a PPV higher than 0. 95 for all but one method. They also perform well on the recall side, where all methods achieve at least 0. 9 of TPR. OMA Groups and PANTHER LDO achieve the best precision (PPV: 0. 996) with slightly lower recall (TPR: 0. 914 and 0. 922 respectively) than other methods while OrthoMCL has the best recall but at the cost of a PPV being more than half of the other methods (PPV:0. 443, TPR:0. 983). Most other methods are on or close to the Pareto Frontier between the two extremes, keeping a PPV close to 1 and varying mostly on recall. Domainoid and OrthoFinder are the two methods that appear to get the best trade-off in this regard, keeping their PPV close to 1 (respectively 0.
ADDING VALUE TO QFO SUBMITTED DATASETS VIA OpenEBench AND EUDAT B2SHARE Stable long-term data sharing is fundamental for guaranteeing results reproducibility and enabling data reusabil-ity in downstream analyses. Indeed, for the 2020 installment, OpenEBench (28) has upgraded its underlying data infrastructure. Specifically, OpenEBench now makes use of B2SHARE (https://b2share. eudat. eu/) for long-term availability and storage of publicly available QfO datasets. B2SHARE is the repository service for sharing research data of the EUDAT Data Collaborative Infrastructure (https://eudat. eu/), one of the largest e-infrastructures in Europe offering permanent storage capacity and integrated management services for research communities. Access to EUDAT records is possible through the links on the Public Projects page of the Benchmarking service, by clicking on the Uploaded data link.
There one can access the metadata and download the predicted orthologous pairs. One of the key reasons for adopting the EUDAT B2SHARE technical standards, data models and policies is that it helps OpenEBench to further enforce its FAIR data principles (29) by design compliant strategy. Indeed, when using B2SHARE, QfO datasets become on one side Findable, through the assignment of permanent identifiers, as Digital Object Identifiers (DOI), and on the other Accessible, through the EUDAT infrastructure, adding value and visibility to QfO participant submission. Importantly, ownership is always preserved, as OpenEBench acts on behalf of the participant. Beyond QfO public orthology prediction, OpenEBench includes a variety of dataset types as part of the evaluation benchmarking workflows. Those datasets cover the reference data used in benchmarking events as gold standards, the already mentioned submitted predictions and, also, the actual results scoring and comparing participants, provenance reports and metrics comparison plots.
Nucleic Acids Research, 2022 5 Figure VGNC symbol Benchmark. Results of each method with True Positive Rate (x-axis) as a recall measure and Positive Predictive Value (y-axis) as a precision measure. Note: The axes have been truncated to maximize the spread of the methods on the figure. As a result, the y-axis shows a significantly larger variation in values than the x-axis for easing data discovery, so thus submitted collections could be annotated with cross-links to OpenEBench for further insights. To reflect this new functionality and other improvements, the OpenEBench documentation (https:// openebench. readthedocs. io/) has been extensively rewritten, facilitating the platform usage according to the different user profiles.