dataesr/scientific-paragraphs-categorization huggingface dataset:
Relying on the method of  Georgoulis et al. (2012) ,  Kontogiannis et al. (2017)  implemented the computation of the non-neutralized electric currents within the FLARECAST property database. Calculations for a sample of AR time series showed with statistical significance that the systematic increase in the amount of non-neutralized currents concurred with the development of strong PILs and signaled phases of intense flaring. Further application to a representative sample of Solar Cycle 24 ARs showed that the total unsigned nonneutralized current of an AR is promising in distinguishing between flaring and non-flaring AR populations within a 24-hour time window  (Kontogiannis et al. , 2017). 'Promise' in that work implied a significantly better (i. e. , beyond applicable uncertainties) ability to distinguish between flaring and non-flaring active regions than the (often used as baseline) unsigned magnetic flux -see Figure  10  and discussions in  Barnes and Leka (2008) ;  Georgoulis (2012 Georgoulis ( , 2013)).
Flare prediction and feature ranking The FLARECAST machine-learning prediction component draws from and relies on the plethora of predictors extracted from NRT HMI SHARP magnetograms populating the project's property database. This central objective has been realized by  Benvenuto et al. (2018) ,  Florios et al. (2018) ,  Piana et al. (2019)  and  Campi et al. (2019) , while the overall process has been described and discussed by  Massone et al. (2018). Benvenuto et al. (2018)  tested an array of supervised and hybrid (i. e. , comprising both supervised and unsupervised elements; see Section 2. 3) machine-learning methods on historical NOAA/SWPC sunspot classifications between August 1996 and December 2010, training the methods on similar data between December 1988 and June 1996. They relied on flaring rates associated to certain sunspot classes, similar to  McCloskey et al.
(2016) , but using machine learning for prediction. The main result was that hybrid methods tend to outperform strictly supervised ones and approach the performance of clustering methods. Benvenuto et al. (2018)  used a variety of metrics and skill scores present in Table  4  and further determined that a reliable feature ranking by means of their prediction value is possible. Florios et al. (2018)  used three different supervised methods (i. e. , MLPs, SVMs and RFs; see Table  2 ) on a subset of the FLARECAST property database over a five-year interval covering 2012-2016. They were able to correlate between the different methods and concluded that RFs  (Breiman, 2001 ) could be the method of choice in a routine, operational flare forecasting scheme. Feature ranking was performed by means of different scores and indices and it was found that peak performance could be achieved by using 10 -12 predictors in total.
Both purely probabilistic and probability-thresholded categorical performance verification was performed, with optimal probability thresholds determined. The overall performance practically matched, and in some cases exceeded, that of  Bobra and Couvidat (2015)  who used a SVM and performed the first flare forecasting work using HMI SHARPs. The study of  Campi et al. (2019)  performed a data-and property-intensive flare prediction investigation. It utilized a database of 14,931 point-in-time property vectors, each comprising up to 171 predictors within the interval September 2012 to April 2016. The main objective was to determine whether, and to what extent, this abundance of predictors is essential or redundant in flare prediction. The study utilized two machine-learning methods, namely hybrid LASSO and RFs, and opted to construct four training and four testing sets, each corresponding to a 24-hr forecast window from a specific issuing time (i.
(2019). In fact, the prediction method itself may often be less important than the robustness of training and testing practices. By roughly reproducing the training and testing of  Bobra and Couvidat (2015)  and  Florios et al. (2018 ), Campi et al. (2019)  were able to practically match the performances of these studies for different machine learning methods. As a result, the 'uncompromised' stochasticity of flare occurrence, as coined by  Campi et al. (2019) , could not be curbed even with one of the largest-dimensionality parameter spaces. Given the above, the FLARECAST performance verification infrastructure (Section 4) is in place and allows the evaluation of performance of any future effort than can rely on either the existing or enhanced property database (Section 2. 2) and the prediction database (Section 2.
3). Obviously, the quest continues. At this point it is important to clarify that, although FLARECAST valued highly the use of predictor time series for flare forecasting, this important part of the analysis did not have the time to materialize during the project's nominal period of execution. The only exception is the forthcoming work of  Gontikakis et al. (2020)  that exploits DEM time series to uncover flare predictors or precursors. Time series, in terms of flare history and / or predictor evolution, have been found useful in flare prediction  (Leka et al. , 2018 (Leka et al. , , 2019b;; Park et al. , 2020)  on top of point-in-time forecasting. While this avenue is yet to be investigated with the FLARECAST databases, these data clearly enable one to pursue this objective in the future.
The flare-CME connection that could advance CME prediction was investigated via a unique synergy between FLARECAST and an EU Framework Programme 7 project funded under the 'Exploitation of space science and exploration data' Call. The HELCATS project created numer-  ous data products from heliospheric imaging onboard the two NASA STEREO spacecraft in order to track the evolution of CMEs in the inner heliosphere. Using the project's main catalogue of over 2,000 CME events imaged between 2007 and 2017, an automated algorithm was developed to connect the CMEs observed by STEREO to any corresponding solar flares and AR sources on the solar surface, resulting in the HELCATS LOWCAT catalogue. Murray et al. (2018)  supplemented the LOWCAT catalogue's information on CME kinematic properties (such as speed and angular width) with FLARECAST's extensive property database derived from vector magnetograms, enabling a deeper study into the characteristics of eruptive ARs.
The selection intentionally focused on PIL-related properties, including some well-established ones (such as Schrijver's R, WL S G and B eff ) along with ones implemented in the course of FLARECAST (such as the Ising energy and the total non-neutralized electric current; see Table  1 ). CME information was collected from NASA's DONKI database, utilizing CME images by LASCO onboard the SOHO mission. Positive correlations between CME kinematic properties were found for all pre-eruption photospheric properties, in line with the results of  Murray et al. (2018). Remarkably, correlations were stronger for faster CMEs implying that selected non-potentiality properties may be indicative of upper-limit eruption scales that ARs can produce over a given timescale. Among the FLARECAST properties tested, the preflare total unsigned non-neutralized current and the length of the main PIL stand out, exhibiting significantly stronger correlations with the CME kinetic energy and speed (Figure  13b ).
, 2002; Barnes, 2007; Ugarte-Urra et al. , 2007; Georgoulis, 2008) , all show promise toward a viable interpretation of the pre-eruption configuration in solar ARs and a deeper, more complete understanding of solar eruptions. Along with other recent studies  (Barnes et al. , 2016; Leka et al. , 2019a,b; Park et al. , 2020) , FLARECAST has showcased the need for fixed, pre-defined data sets in solar flare (and eruption) forecasting. Such 'benchmark' data sets (see  Angryk et al. , 2020 , for a definition and an example) enable the precise evaluation of different methods, machine / deep learning or other, on preset training and testing samples. Performance comparisons on different data sets and / or phases of the solar cycle can be problematic due to the differences in underlying flare statistics over different time periods, so a benchmark dataset should be partitioned in such a way as to treat variable climatology to the extent possible.
The FLARECAST property database could also play the role of a benchmark dataset. It further enables the important action of time series forecasting, that was unfortunately not achieved during the project's nominal period of execution, and the treatment of class imbalance in machine learning performance. Such investigations directly address another trait of major flares, namely their scarcity and rareness, that becomes extreme for flares of historic magnitudes. FLARECAST developed an infrastructure that could help address the need for an integrated space weather forecasting platform. Coordination of space weather forecasting efforts has been identified as a key priority in proposed roadmaps such as the one developed on behalf of the COSPAR  (Schrijver et al. , 2015). It is also prominently recommended by the ESWACC  (Opgenoorth et al.
, 2019). In line with these studies, global coordination of space-weather forecasting efforts sprung out as a top-level conclusion during the FLARECAST Stakeholder Workshops. The project's infrastructure could serve as a testbed, or breadboard, for future applications encompassing the other two main legs of "stormy" space weather, namely CMEs and SEPs. Given the versatility of the Docker engine and containers used in FLARECAST, components of different programming languages can be removed, updated, or added in a highly modular fashion. All in all, FLARECAST has amply shown that solar flare forecasting should not be, and thankfully is not, an internal affair for heliophysicists. Fusion of expertise is paramount to advance and, ultimately, break ground toward an efficient space-weather forecasting but also other complex realworld problems.
-In Consortium deliberations, common understanding and a continual pursuit of consensus in a collegial atmosphere were valued as the most important elements for a successful collaboration. -In a project with strict requirements for hardware installation and software development, time (and backup time) is necessary; efficient time management is paramount for the aversion of implementation delays. Given the high degree of synchronization required between parallel tasks, delays often seem inevitable; when they happen, they may also cause a domino effect affecting different project parts. Advance risk assessment and time planning help mitigate the impact of these delays. The most useful View route of the prediction database service is: • /prediction/list v2, that provides the main access to the prediction database.
As with the property database, various options can be used to refine the search. As an example, an API URL for all available predictions within the 5-month interval of 1 January 2018 to 1 June 2018 reads https://api. flarecast. eu/prediction/prediction/list_v2?include_flare_ associations=true&algorithm_config_version=latest&prediction_time_start= between(2018-01-01,2018-06-01). The search window can be adapted by prediction time start. Each entry provides the algorithm configuration name (algorithm config name) and id (algorithm config id), along with all other applicable information. One can look into certain algorithm configuration names to refine the search further by setting the algorithm config name field accordingly and obtain the respective API route. Most other routes in the property and prediction database services are still used by some of the user interfaces and some debugging scripts, but are not intended for end users.
A. 2. Codes All FLARECAST source codes and computational infrastructure are public and can be found in the project's Bitbucket repository: https://dev. flarecast. eu/stash/projects/. The repository includes different directories, namely: -Download: algorithms for downloading SWPC and flare staging data. -Property Extraction: codes and algorithms for the extraction of all properties included in Table  1. -Flare Prediction: source codes for the machine learning algorithms included in Table  2. -FLARECAST Data Model: codes for defining and implementing the project's data model (Section 2. 4. 8). -FLARECAST Infrastructure: codes for the project's computational infrastructure (Section 3). Algorithms typically include brief usage notes, examples and information on each applicable programming language.
A. 3. Infrastructure installation The first dependency is the installation of a Docker engine from https://www. docker. com. Docker is an open source software that offers an ecosystem in which different Docker containers co-exist and function independently. Given that each FLARECAST Docker container contains a different routine or algorithm written in different programming languages (IDL, Python, C, or R), the second dependency is that the installation server must be equipped with licenses of these languages to be able to edit / modify the infrastructure. With Docker engine installation in place, the FLARECAST infrastructure is installed in a Unix/Linux command line environment as follows: -Checking the impact of space weather disturbances on GNSS systems. -Allowing the spacecraft operator to tailor services for spacecraft control aspects.
(a) SHARP HMI metadata (i. e. , properties); (b) data set of SHARP-calculated properties exclusive to FLARECAST; (c) NOAA/SWPC SRS data; (d) flare association as extracted from the NOAA/SWPC event list. 2. Machine learning configuration database: It contains information learned by a given machine learning method from a given training set according to the machine learning algorithm category. We have generated the following machine learning algorithm categories and corresponding parameters to be saved and stored: (a) Neural Networks → architecture, synaptic weights; (b) Clustering methods → clusters' centers; (c) Regression methods → predictors' weights, regularization parameters. 3. Machine learning result database: It contains the prediction results associated to a given AR property set (i.
Acknowledgements. The  FLARECAST  project was funded by the  European Union Research and Innovation Programme  under Grant Agreement no. 640216. The project would not have been possible without  NASA 's SDO mission and the unrestricted access to SDO data, primarily from  Stanford University 's JSOC. While we wish to sincerely thank all community members who participated in the public discussions, debates and the  Stakeholder Workshops , the  FLARECAST Consortium  is particularly grateful to the  EU -assigned Project Reviewer,  Thierry Dudok de Wit , and the  EU  Project Officer,  Sabri Mekaoui , for their invaluable and continuous help and support. The project's administrative component has benefited enormously by the administrative structures of all partners, coordinated flawlessly by the Project Manager,  Vangelis Argoudelis.
Recognising the threat to humanity that this decline poses, governments and international organisations have responded with strategies to protect and restore biodiversity, such as the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services (IPBES 2022). These and other initiatives also recognise the important roles that genetic and genomic data can play in biodiversity assessment, monitoring, conservation, and restoration, to ensure the long-term health of ecosystem services  (Hoban et al. 2020 (Hoban et al. , 2021;; Formenti et al. 2022). This requires infrastructures that make it easier for scientists to exchange knowledge and agree on best practices, as well as to find, share, and connect increasingly large and diverse datasets. As an intergovernmental organisation that develops services and technical solutions to integrate and coordinate life science resources from across Europe, ELIXIR recognises that connecting molecular sequence data with biodiversity research infrastructures will be critical to support global efforts to prevent further declines of biodiversity.
Molecular data collection initiatives are equally as varied, aiming to collate growing amounts of DNA and RNA sequence data, often also employing a taxonomic framework and collecting sample metadata. Notable examples include the principally archival International Nucleotide Sequence Database Collaboration (INSDC)  (Arita et al. 2021 ) comprising the DNA DataBank of Japan (DDBJ), the European Nucleotide Archive (ENA), and the United States National Center for Biotechnology Information (NCBI) GenBank, as well as the China National GeneBank DataBase  (Wang et al. 2019). More specialised initiatives focus on e. g. ribosomal RNA collections  (Glöckner et al. 2017; Santamaria et al. 2018; Nilsson et al. 2019) , microbiome resources  (Mitchell et al. 2019) , or metagenomics sequence data  (Meyer et al.
2019). These examples help to formulate more formal definitions: (i) molecular sequence data collection initiatives are producing and collating reference catalogues and associated sequencing datasets (metagenomic and metabarcoding) of genetic and genomic biodiversity on Earth; and (ii) biodiversity research infrastructures are capturing knowledge from scientific collections, observations, and the literature, and building resources of biodiversity information for all Earth's REVISED Amendments from Version 1   The revisions to the manuscript encompass edits to the text to clarify points raised by reviewers as ambiguous or unclear, as well as reformulations of some sentences and phrases to improve readability, as requested by the reviewers. Figure  1  was updated with a minor edit to the included text labels.
2015; Mukherjee et al. 2017). Examples such as the Genome Taxonomy Database  (Parks et al. 2022)  are showing how prokaryotic (meta) genomics can aid in improving taxonomies. Others apply metagenomics approaches and are driven more by ecosystem ecology than phylogeny, including the Earth Microbiome Project (EMP)  (Gilbert et al. 2014 ), Tara Oceans  (Sunagawa et al. 2020 ) and other marine surveying projects. Many are driven by the impacts of microbes on human health, e. g. the Global Microbial Identifier (GMI) consortium  (Aarestrup et al. 2012 ) collates genomic information of microorganisms linked to epidemiological data for bacteria, viruses, parasites, and fungi, and the Human Microbiome Project that focuses on host-microbiome interactions (iHMP Research Network Consortium 2019). Similarly, the Global Virome Project aims to improve understanding of the diversity and ecology of viral threats  (Carroll et al.
Taxonomically annotated molecular catalogues of Earth's biodiversity provide the means to scale up data collection of species and community EBVs that can be extrapolated from sequencing georeferenced samples. DNA barcoding has proven to be a cost-effective way of providing a model for integrating genomic data resources and biodiversity catalogues. For example, connecting GBIF with the UNITE database, a fungi-focused DNA barcoding resource  (Nilsson et al. 2019) , enables spatial and temporal surveying even for 'dark taxa' without any physical specimens or resolved taxonomic names  (Ryberg & Nilsson 2018). Another example is the DNA barcode reference library of Canadian invertebrate fauna, which is supported by voucher specimens, digital images, and DNA extracts, with sequences deposited at GenBank and BOLD, and specimen data contributed to GBIF as Darwin Core records  (deWaard et al.
2019). Beyond barcodes, employing the MGnify resource  (Mitchell et al. 2019)  to perform taxonomic assignments of microbiome sequencing data, the European Molecular Biology Laboratory European Bioinformatics Institute (EMBL-EBI) and GBIF teamed up to facilitate the generation of sequence-based occurrence records from georeferenced European Nucleotide Archive (ENA) samples as standardised Darwin Core sampling-event datasets  (Schigel et al. 2019). Facilitating these processes is important to ensure that DNA-derived data are made discoverable through biodiversity platforms and thus increase the value of sequences with associated coordinates and timestamps  (Andersson et al. 2021). These examples show that making such connections can (i) extend traditional sampling methods of observing, capturing, or extracting, to massively scaled-up sampling using metagenomics or environmental DNA (eDNA) techniques; and (ii) transform traditional expert identification approaches into super-fast molecular species identification using progressively more comprehensive reference sequence databases.
They inform strategies for developing national biodiversity programmes, while also highlighting key gaps that need to be addressed. Together with other examples, they help to formulate recommendations for closer integrations through ELIXIR and other infrastructures that will shape the future of biodiversity research. ELIXIR as an infrastructure to support integration of molecular and other biodiversity-related data ELIXIR is an intergovernmental European organisation that brings together life science resources including databases, software tools, training materials, cloud storage and supercomputers, to connect and unite infrastructures vital for scientific research (ELIXIR 2022a). It coordinates, integrates, and sustains bioinformatics resources across its member states, enabling users in academia and industry to access services that support scientists to exchange expertise and develop best practices, as well as to find and share the accumulating volumes of data being generated by publicly funded research.
Ideally, increasing usage of type specimens for both morphological descriptions and DNA vouchers or seeking taxonomists expertise will reduce such misidentifications, and better connected legacy information will help to identify and correct existing errors. The NCBI taxonomy database  (Schoch et al. 2020 ) offers well-structured taxonomic classification reports in which 'synonyms' and 'equivalent names' are linked to the unique taxID of the main taxon scientific name (Figure  1a ). In particular, the release of February 10 th 2021 contains 179,314 declared synonyms and 1,180 scientific names with more than one taxonomic path or rank. For instance, Diplura is the scientific name of both order and genus ranks, Centipeda is a genus name belonging to plants and firmicutes, and Taenidia is a Coleoptera subgenus and a genus name belonging to plants.
g. the original description of the honey bee Apis mellifera by Linnaeus (1758). In this last issue, text mining techniques would play an important role in collecting the relevant information from scientific literature to update the knowledge needed to resolve such ambiguity. For example, Plazi (2022) extracted over 370,000 taxonomic treatments and data therein including taxonomic treatment citations  (Miller et al. 2015). These data are FAIR and reused by GBIF and accessible through Plazi's application Synospecies, providing access to the taxonomic names and synonyms as linked open data. They are also submitted once a day to NCBI, albeit only data covering organisms already present in the database, and thus morphological based species without molecular sequence depositions are discarded. An additional source of information on taxon names used in scientific publications falls outside taxonomic treatments, such as linked supplementary data tables (e.