dataesr/scientific-paragraphs-categorization huggingface dataset:
Browsing the data and filtering options GlobaLID provides several filtering and selection options (Figure  3 ). (1) The map itself has a jump-to function based on latitude and longitude. (2) Options are present to select only data produced in a chosen time interval or using/excluding specific analytical techniques (e. g. , thermal ionization mass spectrometry (TIMS) vs. multicollector-inductively coupled plasma mass spectrometry (MC-ICP-MS)), or for which precise geographic coordinates are available. (3) The data can be filtered according to (4) geographic location-for example, by country, province, mining district and mining site. (5) The database or any filtered subset can be additionally filtered for geological period, ore minerals and commodities. (6) A slider allows F I G U R E 2 'Map' page of the GlobaLID application (screenshot) the datasets to be selected by publication year.
Geochemical model parameters In the original data publications, if any, typically only the calculated parameters for one of the Pb age models are given. Because the solver functions of different programs yield slightly different results, age model parameters were (re)calculated with the four different geological models mentioned above for all data to ensure their comparability. Similarly, not all Pb isotope ratios in the database were included in the original publications and missing values were therefore calculated to ensure full compatibility of the data. The respective R scripts are available for download in the 'Resources' menu. The application can also be used as a calculator for the age model parameters and Pb isotope ratios, as these values are automatically calculated for temporarily uploaded user data.
DISCUSSION In the pioneering days of Pb isotope provenance studies, only few highly specialised groups produced data. The available data could therefore be easily managed on a personal computer. With analytical developments and the advent of novel instrumentation, but also with the growing number of analytical facilities, the amount of generated Pb isotope data drastically increased. It quickly became obvious that the Pb isotope 'fields' defined in binary plots were not as clearly distinguishable as previously assumed  (Artioli et al. , 2020; Killick et al. , 2020; Pollard, 2009). The isotope signatures of several ore deposit districts show considerable overlaps due to their relation to supraregional geotectonic events naturally not adhering to political borders. Lead isotope provenance studies therefore necessitate a thorough understanding of the mineralogy, and metallogenic and geotectonic evolution of the ore deposits.
g. ,  Bolhar et al. , 2020). They have a lower precision than MC-ICP-MS, but the data are still used for regional studies where high-precision data are not yet available. New U/Pb isotope data on cassiterite is being generated by laser ablation mass spectrometry (LA-MS). Nanosecond LA-ICP-MS data of Pb isotopes (e. g. ,  Neymark et al. , 2021)  has a low resolution compared to solution MC-ICP-MS, but femtosecond LA-ICP-MS promises better data. A novel KBr technique for complete dissolution of cassiterite  (Tapster & Bright, 2020)  will soon allow the production of Pb isotope data with a much higher resolution. These spatially resolved cassiterite analyses will be added to the database when common lead data are reported. Analytical uncertainties are deliberately not included in the database.
GlobaLID leaves the choice of preference to the user. 204-based ratios are strongly correlated because the 204 Pb abundance is much smaller than the abundance of the other isotopes  (Albarède et al. , 2004). Intentionally, 90% ellipses are not offered and are replaced by density plots with polygons of adjustable quantiles. With truncation of values, it is impossible to avoid misleading representations of the reference data, which means that the plotting range is always adjusted to include all data points. The visualisation of both user and reference data adheres to a set of conventions with regard to shape and colour, for example (ores: coloured symbols; user data: black symbols). This standardised layout renders the plots intuitively readable. GlobaLID allows a versatile application of Pb isotopes for metal provenance studies.
CONCLUSIONS GlobaLID honours the heritage of OXALID and the recently published IBERLID database by adapting the concept of a reference database to modern requirements of open data and interactivity. This follows the appeal of G. Artioli, C. Canovar, P. Nimis, and I. Angelini: The small step that the Isotrace Laboratory in Oxford did when making OXALID publicly accessible had enormous consequences for the diffusion of LI methods to research groups not able to develop internal databases. If we want to move forward, it is now time for all archaeometallurgists to share information and enter in the regime of big data analysis. (Artioli et al. , 2020, p. 75)  We successively (re)digitalise and homogenise the large amount of scattered data from original publications or from previously collected and published lists.
GlobaLID intends to have a global coverage of Pb isotope data for ores and aims to prevent a tedious search for isolated digital databases. Published data will be continuously integrated into the current database, especially the compilation of meta-information retrieved from the original publications or additional literature, which firmly embed the data in their geological context and render them valuable in research outside archaeometry as well. However, the scope of GlobaLID is beyond simple data collection and storage in a state-ofthe-art interactive format. The application is designed as a digital platform for sharing Pb isotope data from around the world. Involvement of the research community can make the database truly global. The application comprises multiple tools for dynamic visualisation of geolocalised reference data in several presentation modes, both as isotope ratios and age model parameters calculated according to different models.
It offers users the possibility to integrate their own data into the Pb isotope diagrams and to easily create and export these plots in ready-to-publish formatting. Published datasets including references of original publications can be downloaded. Database and application are designed as an open-access project. The research focus of the core group, the active inclusion of contributors and the affiliation of the first author enable long-term maintenance of both database and application. The implementation of the database by an institution with a long research tradition in georesources, archaeometry and archaeometallurgy gives the project a long-term future perspective for curation and progressive expansion. The community is particularly asked to contribute new data and to augment metainformation of the database with their geological and archaeometallurgical expertise.
Although a large span of regions is already covered, there are still many blank spots on the map due to language barriers (e. g. , the Russian literature), lack of existing data or missing research activities of the GlobaLID core team (e. g. , Africa, Southeast Asia, Central Asia, India, or Meso-and South America, the latter only exemplarily investigated by, for example,  MacFarlane & Lechtman, 2016). Revision of the meta-information already included in GlobaLID by colleagues who are more familiar with the geology or additional information of specific regions is hence greatly welcomed. The database also aims at offering the scientific community a repository to make unpublished data available. GlobaLID's profit for future science is hence based upon intrinsic motivation and collective open data development.
Potential for future development of the database, which is so far focused to suit the comparison with inorganic material in archaeology, is the expansion to metallurgical remains, archaeological material, alternate or supplementary isotope systems (e. g. , Cu, Ag, Sn, Fe, Zn isotopes), or elemental composition, which can be easily integrated into the existing structures. Their possible implementation will, however, depend on the needs and resonance of the archaeometric community. The combination of an enriched and up-to-date database combined with a user-friendly application, which is tailored for but not restricted to the use of metal provenance studies, will attract broader attention to this particular research field. It has the potential to serve the needs of a variety of research interests on natural georesources.
the datasets for Iran and added meta-information. Hannah Zietsch (DBM) organised the publication lists. Andreas Hübner (GFZ data services) advised us in copyright questions, and Annette Hornschuch (DBM) in database matters. Helge Wiethoff (IT services of DMT-LB) set up the server for the implementation of GlobaLID. Comments from F. Albarède on a previous version of the text greatly improved the manuscript. We thank the reviewers for their very positive feedback. Their suggestions and comments enabled us to improve our manuscript considerably.
ACKNOWLEDGEMENTS This work was accomplished over years of own research and profited from the generous sharing of published Pb isotope datasets by many colleagues. The creative and collective work of compiling the presented database and application was initiated when the core group (the authors) were members of the Archaeometallurgy group at the  German Mining Museum (DBM). Eveline Salzmann  was involved in the initial stage of the project. Nima Nezafati  (DBM) revised.
We present Microseek, a pipeline for virus identification and discovery based on RVDB-prot, a comprehensive, curated and regularly updated database of viral proteins. Microseek analyzes metagenomic Next Generation Sequencing (mNGS) raw data by performing quality steps, de novo assembly, and by scoring the Lowest Common Ancestor (LCA) from translated reads and contigs. Microseek runs on a local computer. The outcome of the pipeline is displayed through a user-friendly and dynamic graphical interface. Based on two representative mNGS datasets derived from human tissue and plasma specimens, we illustrate how Microseek works, and we report its performances. In silico spikes of known viral sequences, but also spikes of fake Neopneumovirus viral sequences generated with variable evolutionary distances from known members of the Pneumoviridae family, were used.
Indeed, it is important to identify new viruses that may be very different from known ones, for example in arthropod vectors or in wild animal species, notably to anticipate risks of emergence. Discovery of novel viruses is also critical for viral safety issues of commercialized biological products, in which broad range identification of viruses that could be introduced from human or animal raw materials is essential  [5] [6] [7]. In the past years, many dedicated platforms and software were developed. Some of them run on dedicated computational infrastructures  [8] [9] [10]  while others directly operate in the cloud, an option which is not always compatible with the confidentiality of medical and industrial data  [11, 12]. Furthermore, in such bioinformatics tools, databases are often generalist, may include incomplete genomes and may be of lesser stringency  [13].
In this context, full exploration of sequence data relies on developing flexible search tools that may bridge existing gaps among available solutions. Viruses 2022,  14, 1990  2 of  20  Here we present Microseek, a software that is primarily oriented to discovering new viruses while efficiently detecting already known ones. Our approach is grounded into the following observations: (i) most of the genome sequence of viruses encodes proteins, (ii) several protein domains with specific biological functions are conserved (e. g. , the viral polymerases) as opposed to the degenerated nature of the genetic code, (iii) it is therefore easier to detect evolutionarily distant viruses based on protein similarity. Further, our pipeline relies on the detection of viral protein genes and takes advantage of the exhaustive and curated viral sequence database RVDB-prot  [14] , itself derived from the nucleic Reference Virus DataBase (RVDB)  [13].
Instead of randomly picking one best match, Microseek taxonomic assignment is based on the Lowest Common Ancestor (LCA), providing a more accurate overview of a sample content. When the sequences share a low identity with sequences in the database, the LCA procedure of Microseek relates the sequence to a higher taxonomic group. Such a case is highly suggestive that the sequence may belong to an unknown organism. We evaluated and compared the performances of Microseek to Chan Zuckerberg ID (CZ ID, formerly IDseq), a cloud-based metagenomics platform that was recently compared to many other software  [12]. We used two datasets representative of common clinical metagenomic matrices called Tissues and Plasma. These two datasets were spiked in silico with three concentrations of sequences from six known human viruses representative of the Baltimore classification (ssDNA, dsDNA, ssRNA+, ssRNA-, dsRNA, and retrovirus).
Taxonomic Assignation of Protein Sequences Microseek uses three levels of blast homology searches (called B1, B2, and B3) based on three different databases. This sequential pattern of queries uses relaxed parameters Viruses 2022,  14, 1990  3 of 20 in B1 on a viral restricted database in order to detect distant sequences, then invalidates false positives in B2 and B3 on more generalist databases. For protein searches, we used Diamond blastp  [21]  instead of NCBI blastp tool, as it approximates a blastp with a very low loss of sensitivity while increasing the query speed up to 40,000 times and keeping the same results format and metrics as NCBI blastp. B1: The protein sequences predicted at the previous step are queried using Diamond blastp 0. 9. 26 against the latest release of RVDB-prot (currently 23.
0)  [14] , with a maximum e-value requirement of 5. Tabular output is annotated, subject sequences (i. e. , potential matches) are associated with a given taxon using taxadb. Results are then combined, generating LCA annotation (corresponding to the common ancestor of all the best matches). A script generates sub-sequences that will be used as queries during the next stage: they correspond to the portion that aligned with a match in the database, commonly known as High-scoring Segment Pair (HSP). B2 is the first validation step: NCBI NR protein databank is queried with Diamond, with a maximum e-value of 100 (this value can be defined by user) using the sub-sequences produced during B1 as queries, and further annotated with LCA. Sequences having a LCA that does not belong to Viruses (taxid 10,239) are discarded, and hits corresponding to "artificial sequences" (taxid 81,077) are not considered for the inference of the LCA (else, these sequences would not be considered as viral, as artificial sequence is not inside "Viruses" taxon).
For the remaining sequences, the original nucleic portion of the sequence (corresponding to the ORF aligned to the first B1 match) is then retrieved for the final validation step. During B3, a blastn (Blast+ 2. 6. 0, parameter task = blastn, no criterion applied on e-value) is finally performed using the nucleic sequences produced in B2 to query NCBI NT databank. Only sequences with a viral hit (taxid 10,239) are conserved. This step allows to eliminate sequences that have a better assignation with non-coding sequences and allows, for example, to filter out cellular sequences present in certain viral genomes like in pestiviruses  [22]. These three successive assignment levels (B1, B2 and B3) contribute to the elimination of inherent false positives, without replacing human expertise of the final results.
To try to broaden the range of detection of viruses to very distant viruses, ORFs with no match in B1, B2 and B3 and whose size is greater than a user-defined threshold (default setting: 666 amino acids, i. e. , 2000 nucleotides) are tested against the RVDB-prot-hmm database using HMMER (with-domtblout parameter). The resulting HMM H4 file is a list of homologous clusters with associated keywords and LCA retrieved from RVDB-prothmm annotation database  [14] , along with the best probability scores (best_proba) and the size of the longest matching domain (greater_length).
Output Two raw tables are outputted. They contain, respectively, results of steps B2 and B3, providing several information: original sequence name, original nucleic sequence, translated sequence; and information about B1 and B2 results: e-value, LCA, percentage of amino-acid identity, difference of taxonomic level assignation between B1 and B2, length of queries that matched with sequences contained in the database (query coverage), and protein types. In addition, the presence of an ORF encompassing the whole sequence length is reported, as it can help to differentiate exogenous viruses from endogenous viral sequences. Indeed, viral sequences integrated in host genomes are not constrained by selection pressures and often contain stop codons. The information of ORFs that cover the whole sequence is, therefore, a good indicator that the identified virus sequence does not correspond to an integrated viral sequence, which are frequent in arthropod genomes for example.
Matrix Datasets Two datasets from pre-existing clinical metagenomic experiments, reflecting two different types of matrices, were used. The first one, called Tissues, was derived from transcriptomic data generated from human biopsies, from which sufficient nucleic acids input can be extracted to construct NGS libraries, which reduces the need for amplification procedures and subsequent co-amplification of a background of unspiked sequences from laboratory components  [24]. This dataset was composed of a random selection and mixing of 50 million reads generated with the SMARTer Stranded Total RNA-Seq Kit v2-Pico Input Mammalian (Takara) from the transcriptome analysis of 4 human biopsies (kidney, liver, nymph node and brain) in which no known human pathogen was found. The second dataset, called Plasma, reproduced a virome analysis from human plasma, with low nucleic acids yields after extraction of nucleic acids after hydrolysis of nonencapsidated nucleic acids, which needed random pre-amplification before library construction.
In addition, phages populations plus other probable translocation of viral sequences in blood made the plasma to have a much lower host vs. non-host sequences ratio leading to an over-amplification and detection of a background made of unspiked sequences. This second dataset consisted of a random selection and mixing of 50 million NGS reads generated from two human plasma in which no known human pathogen was found and was generated from total nucleic acids (DNA and RNA) with the NEBNext Ultra II DNA Library Prep Kit (NEB) after a reverse transcription and pre-amplification phases using MALBAC  [25]. For both datasets, only reads of good quality after trimming were kept. Names of the original reads were modified, and reads were mixed-up to guarantee personal data protection.
The following principle was followed in the Tissues dataset, which mimicked a transcriptome approach (RNAseq), both genomic and messenger viral RNAs sequences served as template in the proportion 90% and 10%, respectively, to reflect the content of active replicating viruses. For the Plasma dataset, which mimicked a virome approach where viral DNA and RNA protected by capsids are sequenced, only the genomic sequences served as a matrix, reflecting the content of viral particles. From each viral sequence, spiked fastq reads were generated using ART version Moun-tRainier 2016-06-05 (accessed date: 28 July 2022)  [26]. ART produces in silico sequencing reads that respect pre-determined error profiles, which could either be built-ins or created from a user-provided fastq file (with the tool art-profiler-illumina, provided by the ART package).
The latter option was used to generate two error profiles, respectively, for the Tissues and Plasma datasets, according to the quality of the reads of each dataset. Then, art-profiler-Illumina was used to produce the defined number of reads following the rules defined below. Generated reads were finally spiked in the corresponding datasets, at a random position of the fastq file. Viruses 2022,  14, 1990 5 of 20  Three in silico viral spiking loads, namely dilution 1 (d1), dilution 10 (d10) and dilution 100 (d100) were set-up. The number of spiked sequences at d1 was calibrated to mimic clinical viral loads of 10 5 copies/100 mg of tissue  [27]  and of 10 2 -10 3 copies/mL of plasma as previously assessed with reference spike viruses  [28]. Detailed rules and proportion of spiked sequences are presented in the Supplementary Table  S1.
Data and Software Availability The following material is available online at Zenodo (https://doi. org/10. 5281/zenodo. 6937000 (accessed on 28 July 2022); Creative Commons Attribution 4. 0 International License). This record provides seven archives: raw fastq files corresponding to the unspiked (2 datasets); spiked with known viruses (6 datasets); spiked with Neopneumoviruses (12 datasets); additional tick sample used in discussion and negative control experiments; Neopneumovirus sequences (fasta files) and all Microseek outputs (40 folders).
Overview of the Pipeline Architecture We present Microseek, a pipeline with a graphical interface for virus identification and discovery to be run with Snakemake  [29]  (Figure  1 ). It takes advantage of a welldefined, curated and regularly updated reference database made of protein viral sequences (RVDB-prot  [14] ). As so, after common QC and first-step procedures consisting mainly in Viruses 2022,  14, 1990  6 of 20 normalization and sequences assembly into contigs, the first specific step of the pipeline is the translation of the nucleotides sequences into coding amino-acid sequences and the search for similarity within the RVDB-prot database (Figure  1A , step B1). Hits are then filtered applying user-defined e-value criteria and the specificity of the remaining sequences is validated against the entire NCBI NR database (Figure  1A , step B2).
A final blastn procedure on the NCBI NT database (Figure  1A , step B3) mainly aims to exclude host sequences recombined in viral genomes, which would have presented alignment homologies with viral sequences in the previous steps. Microseek consider all co-best hits, summarized by their LCA. At the end, a visual representation based on Krona is given (Figure  1B, D ), together with a home-made HTML interface which allows for easy sequences selection, filtering and retrieving directly from Krona, along with public annotations (taxonomy, related accession numbers, protein names) provided (Figure  1C, E ). All the buttons are clickable, either for data sorting or selection. For example, all sequences of a given taxonomy can be selected, or all sequences of a given protein type, or any combination can be sorted according to their size or identity percentage.
Overview of the Pipeline Architecture We present Microseek, a pipeline with a graphical interface for virus identification and discovery to be run with Snakemake  [29]  (Figure  1 ). It takes advantage of a welldefined, curated and regularly updated reference database made of protein viral sequences (RVDB-prot  [14] ). As so, after common QC and first-step procedures consisting mainly in normalization and sequences assembly into contigs, the first specific step of the pipeline is the translation of the nucleotides sequences into coding amino-acid sequences and the search for similarity within the RVDB-prot database (Figure  1A , step B1). Hits are then filtered applying user-defined e-value criteria and the specificity of the remaining sequences is validated against the entire NCBI NR database (Figure  1A , step B2).