dataesr/scientific-paragraphs-categorization huggingface dataset:
This shows that in a medium-sized city such as Marosvásárhely, anti-COVID measures have caused a decrease in land surface temperature in specific areas (Fig. 11 , Tables  2  and 3 ). The MODIS LST data are satisfactory for identifying general trends and patterns at global or moderate geographical scale. However, for a hotspotcoldspot analysis of the urban heat islands, it is more appropriate to use Landsat satellite data. Limitations of the study: To show irrefutably that the anti-COVID measures had a significant effect on the decline of LST, more or all medium-sized cities would have to be analyzed at a country level or the European continental level. The nature and effectiveness of the measures should be taken also into account in each country or region. Therefore, other studies should compare more cities in the same administration region or the same country.
3 ). [19]  An efficient implementation of ECD on a TOF MS platform was not available at that time, whereas currently there are several very potent ECD-enabled TOF MS configurations. [17h,32]  The original topdown ETD results delivered 14% and 21% sequence coverage for the human and the murine mAb, respectively. Importantly, product ions were detected from the CDR regions, specifically the functionally critical CDR3 region. Overall, this advance was enabled by the ability of a novel high-resolution mass spectrometer to efficiently transfer heavy precursor ions and many product ions to the mass analyzer and to decipher a complex pattern of product ions. In addition, the already developed initial data analysis methods, including a high-resolution product ion deconvolution algorithm, facilitated analysis of the convoluted MS/MS datasets.
1 ). [46]  Briefly, the experimental measurements of an AOC sample (trastuzumab-oligonucleotide conjugate) were performed on a Q Exactive TM HF TM mass spectrometer equipped with the BioPharma option. [46]  This allowed for ion detection over an extended mass range and thus the use of native conditions for sample preparation, separation (size exclusion chromatography, SEC), and ionization. The unreduced data (time-domain transients) were acquired with an external high-performance data acquisition system (FTMS Booster X2, Spectroswiss) and processed using the advanced FTMS data processing and analysis software package Peak-by-Peak (Spectroswiss). The ability to define the resolution of the final mass spectrum was achieved by a user-controlled time-domain transient truncation process -as FT mass spectral resolution is proportional to the transient period, typically.
[62]  In the case of mAbs, the latter statement is fulfilled if short, less than 60 ms, transient periods are considered. [46]  This period corresponds to the half-width of the first time-domain isotopic beat of a mAb. However, increasing the transient period, even substantially, by 10-fold and more, will not enhance resolution. Again, this is due to the particular structure of the isotopic beat patterns from mAbs in FTMS. [17d,63]  In the above example, the identified optimum mass spectral resolution that enabled estimation of DAR values of the investigated AOC sample was 4-fold lower than the lowest resolution provided by the employed Orbitrap model. It is also 2-fold lower than the lowest resolution provided by any current Orbitrap model. This example illustrates the additional flexibility and intriguing potential toward developing novel methods allowed by the access and possibility to process unreduced FTMS data (time-domain transients).
Acknowledgments We dedicate this article to the memory of  Prof. Fred W. McLafferty  to commemorate his outstanding contributions and achievements in the field of top-down mass spectrometry. We heartily thank  Dr. Unige Laskay  and the other team members of our mAb research program at  Biomolecular Mass Spectrometry Laboratory at Ecole Polytechnique Fédérale de Lausanne (EPFL). We greatly appreciate our collaboration for Sap9 production and characterization with  Dr. Michel Monod  from  CHUV, Lausanne. We also express our gratitude to  Bruker Daltonics  and  Thermo Fisher Scientific  for fruitful collaboration and support. We thank Spectroswiss,  EPFL , the  Swiss National Science Foundation  (grant # 147006  to YOT), and the  European Horizon 2020 research and innovation program 's projects # 829157  ( TopSpec ) and # 964553  ( ARIADNE ) for funding.
In recent years, metabarcoding has become the method of choice for investigating the composition and assembly of microbial eukaryotic communities, and an increasing number of environmental datasets are being published. Although unprocessed sequence files are often publicly available, processed data, i. e. sequences clustered as operational taxonomic units (OTUs) or amplicon sequence variants (ASVs) are rarely at hand in a comparable format. This hampers comparative studies between different environments and datasets, for example examining the biogeographical patterns of specific groups/species, as well analysing the micro-genetic diversity within these groups. Here, we present a newly-assembled database of processed 18S rRNA metabarcodes that are annotated with the PR 2 reference sequence database.
2021; Santoferrara et al. 2020). Metabarcoding reveals the taxa present in an environment by amplifying and then massively sequencing a standardised genetic marker  (Santoferrara 2019; Taberlet et al. 2012). In recent years, it has become a very powerful and widespread approach to investigate protist diversity in a range of environments (marine, freshwater, soils, microbiomes etc. ). By far, the most common marker used for eukaryotic microbes is the gene coding for small ribosomal subunit RNA (18S rRNA). This gene has the advantage of being universal and having well annotated reference databases such as Silva or PR 2  (Guillou et al. 2013; Quast et al. 2013)  which allow, for many protist groups, a precise taxonomic assignation. Within the 18S rRNA gene, several variable regions have been used as barcodes, in particular the V4 region located near the middle of the gene and the shorter V9 region located at its 3' end  (Burki et al.
g. Malviya et al. 2016; Yau et al. 2020) , factors structuring marine plankton communities  (Logares et al. 2020; Sommeria-Klein et al. 2021) , and the seasonal succession of taxa (e. g. Giner et al. 2019; Lambert et al. 2019). Fewer metabarcoding studies have been carried out in freshwater and soils, but that is rapidly changing with some large scale studies (e. g. for soils  Mahé et al. 2017). Most eukaryotic metabarcoding studies have targeted one specific environment, thereby preventing large scale comparisons. On the other hand, large metabarcoding projects using the 16S rRNA gene have metaPR2 database -p. 4 been undertaken such as the Earth Microbiome Project which encompassed more than 23,000 samples of both free-living or host-associated microbes, and allowed inferences of global patterns of prokaryotic diversity  (Thompson et al.
2017). For eukaryotic 18S rRNA, large expeditions for sample collections have been undertaken in particular for marine systems such as Tara Oceans, Ocean Sampling Day (OSD) and Malaspina  (De Vargas et al. 2015; Duarte 2015; Kopf et al. 2015). Many studies that performed analyses on the global ocean microbiota have used one or several of these three datasets, in particular Tara Oceans (e. g. Ibarbalz et al. 2019; Sommeria-Klein et al. 2021). Many more smaller-scale metabarcoding studies have also been carried out, in particular for environments that have been not sampled by these expeditions, such as soils or freshwater lakes and rivers (Lopes dos  Santos et al. 2021). Unfortunately, it is difficult to combine the data from these studies with those of the large scale expeditions for a range of reasons.
First, even if the unprocessed data files containing raw reads have been deposited to GenBank SRA (Small Reads Archive), secondary data, i. e. clustered sequences at a certain similarity level, so-called Operational Taxonomic Units (OTUs), or Amplified Sequence Variants (ASVs,  Callahan et al. 2016 ) that do not depend on a specific similarity threshold, are rarely available or, if available, hard to locate since they are stored in a range of formats (DOCX, XLSX or TXT files) as supplementary files. Second, OTUs clustered with different levels of similarity (e. g. 97 vs 99%) are not directly comparable: if two studies are to be combined, it is necessary to perform clustering again, starting from the raw sequences. Third, taxonomic assignation is often done with different reference databases, such as GenBank, Silva or PR 2  (Guillou et al.
2013; Quast et al. 2013). Some studies have tried to combine sets of samples from different environments (e. g. marine, freshwater and soil,  Singer et al. 2021) , but these efforts remain limited (for example, the  Singer et al. 2021  only included 122 sampling sites). Thus, there is clearly a need to provide the protist research community with a reference database of metabarcodes which would allow the full exploration of the available sequencing data by combining existing studies across different environments. In this paper, we introduce a database of metabarcodes (metaPR 2 ) containing more than 4,150 samples originating from 41 public studies, most using the V4 region of the 18S rRNA gene. In order for the different metabarcodes to be directly comparable, we reprocessed all primary files (except those from the Tara Oceans expedition) with the same pipeline based on the dada2 R package  (Callahan et al.
Dataset selection and metabarcode processing Datasets were selected from published studies (Table  1 ). Raw sequence files and metadata were downloaded from NCBI SRA website (https://www. ncbi. nlm. nih. gov/Traces/study) when available or obtained directly from the investigators. Information about the study and the samples (substrate, size fraction etc. ) as well as the available metadata (geographic location, depth, date, temperature etc. ) were stored in three distinct tables in a custom MySQL database. For each study (except for the V9 Tara Oceans dataset, see below), raw sequences files were processed independently de novo on the Roscoff ABIMS (Analysis and Bioinformatics for Marine Science) cluster. Primer sequences were removed with cutadapt (Martin 2011) using the default parameters (maximum error rate = 10%).
Amplicon processing was performed under the R software (R Development Core Team 2013) using the dada2 package  (Callahan et al. 2016). Read quality was visualized with the function plotQualityProfile. Reads were filtered using the function filterAndTrim, adapting parameters (truncLen, minLen, truncQ, maxEE) as a function of the overall sequence quality. Merging of the forward and reverse reads was done with the mergePairs function using the default parameters (minOverlap = 12, maxMismatch = 0). Chimeras were removed using removeBimeraDenovo with default parameters. For the Tara Oceans dataset, because of the very high read coverage, we did not reprocess the Illumina read files and used the sequences that had been and clustered with the Swarm software  (Mahé et al. 2014 ) as detailed in  de Vargas et al.
(2015). ASVs with similar sequences from different studies were merged together and identified with a unique 10 character code which corresponds to the start of 40-character hash value of the sequence (using the R function digest::sha1). Taxonomic assignation of all ASVs, including those from Tara Oceans, was performed using the assignTaxonomy function from dada2 against the PR 2 database  (Guillou et al. 2013 ) version 4. 14 (https: //pr2-database. org/). ASV sequence and taxonomy, as well as abundance in each sample, were stored in MySQL tables in the same database as the metadata (see above). In order to limit the size of the database, we only considered ASVs that corresponded to more than 100 reads in any given studies. The number of reads in each sample was normalized to 100 such that the read abundances could be expressed as % of total eukaryotic reads in some visualizations (e.
Metabarcode analysis Since the datasets included into metaPR 2 used different sets of primers (see below Table  S3 ), we clustered ASVs with 100% similarity using vsearch -cluster_fast option. ASVs within each cluster were merged together, using the centroid ASV as the new ASV. In order to evaluate the similarity of ASVs to existing sequences, we followed the approach of  Metz et al. (2021). We compared ASVs to sequences from the PR 2 database  (Guillou et al. 2013 ) version 4. 14 (https://pr2-database. org/) using the vsearch -usearch_global function with iddef = 2. The similarity information was stored in the MySQL database and then retrieved and merged with the ASV information using an R script. Alpha and beta diversity analyses were performed using the R phyloseq package (McMurdie and Holmes 2013).
R shiny application All post-processing was done with the R software. The data were extracted from the MySQL database using a custom script and stored in files using the R qs package that allows extremely fast loading of files  (Travers 2021). The data are post-processed using packages dplyr and tidyr. An R shiny application was developed to interact with the database using the following R packages: shiny, DT, shinyvalidate, shinyWidgets and shinycssloaders  (Sali and Attali 2020). Data are plotted using packages ggplot2, treemapify, leaflet, leaftlet. minipie and plotly. Alpha and beta diversity analyses are performed using the phyloseq package (McMurdie and Holmes 2013). The shiny application is available in 3 forms: a web-based application (https://shiny. metapr2. org), an R package (https://github.
Overview of metaPR 2 datasets Forty-one datasets are included in the first version of the metaPR 2 database (Table  1 ). We selected global oceanic datasets (OSD, Malaspina, Tara Oceans) that have been used in numerous publications (e. g. Giner et al. 2020; Ibarbalz et al. 2019; Tragin and Vaulot 2018)  as well as smaller data sets in particular from polar waters which have been little explored. Eleven out of the 41 datasets were sequenced using the 454 technology and the rest with Illumina (mostly 2x250). The vast majority of the 41 datasets used the V4 region of the 18S rRNA gene which is the most used metabarcode to date (Lopes dos Santos et al. 2021), with only two datasets representing the V9 region (Tara Oceans and Argentinian lakes, Table  1 ). The most common primer pairs used for V4 (Figure  S1 , Table  S2  and S3 ) were those designed by  Stoeck et al.
(2010)  and modified by Piredda et al. (Piredda et al. 2017). The V4 metabarcodes varied from 309 bp to 672 bp and were overlapping (Figure  S1 ). The metaPR 2 database contains more than 4,150 samples (Figure  1 ). These samples originate from three major ecosystems: marine, freshwater and terrestrial (mostly soil substrate) (Figure  2 ). Among water samples, different size fractions from pico (0. 2-3 µm) to meso (100-1000 µm) are represented with the majority corresponding to the pico and total fractions (Figure  2 ). Most aquatic samples correspond to surface or euphotic layer. Location data (longitude, latitude) are available for all samples but other metadata, e. g. temperature or salinity, may be missing for some samples (Figure  S2 ). The number of samples per dataset is quite heterogeneous ranging from less than 10 to almost 900 for Tara Oceans (Table  1 ).
The total number of reads analysed is almost 900 million for V9 and above 220 million for V4. The number of reads per dataset is also highly variable ranging from about 3,000 in the older studies sequenced by 454 technology to more than 1 million for Tara V9 (Table  1 ), which explains why overall there are more reads for V9 than V4 despite only 2 datasets using V9. The total number of ASVs was about 79,000. The number of ASVs in a given study ranges from less than 100 to more than 14,000 depending on both the number of samples and the depth of sequencing (Table  1 ). Since different studies have used different primer sets, it is necessary to cluster ASVs with 100% similarity in their shared region, leading to slight reduction of the total number of ASVs from 79,000 to 70,000 once clustered.
Protist composition Overall, the database is dominated by Opisthokonta (Metazoa and Fungi) and Alveolata (Dinoflagellata) (Figure  S4 ). In what follows, we decided to focus on protists and on the V4 region. The focus on protists is justified because the sampling strategy of most datasets was optimal for microbial eukaryotes. DNA from those three divisions not included in protists (metazoa, plants and fungi) were probably unevenly sampled, e. g. plant seeds in soils, multicellular organism, larval stages of metazoa in water environments. The focus on the V4 datasets that contain almost 3,000 samples and 850 sites is due to that fact that the data for the V9 region are dominated by the Tara Oceans dataset, which has been extensively analysed previously (e. g. ,  De Vargas et al. 2015). Protist sequences represent more than 40,000 ASVs (33,000 cASVs once clustered).
In terms of reads and cASVs, the database is dominated by Alveolata, followed by Stramenopiles, Hacrobia, Archaeplastida and Rhizaria (Figure  3 ). Based on number of cASVs, Rhizaria despite their lower read abundance come just after the Stramenopiles. Such large number of Rhizaria unique sequences compared to read numbers has been observed before, possibly linked to higher error rates in regions of the RNA molecule that form secondary structures (2011). The most abundant cASVs (Figure  4A ) belong to dinoflagellates (Gyrodinium), diatoms (Minidiscus, Porosira, Fragilariopsis), cryptophytes (Geminigera, Cryptomonas), haptophytes (Phaeocystis) and green algae (Bathycoccus, Micromonas). The most abundant cASVs are often also the most frequently occurring (Figure  4B  and C), although for example the marine picoplanktonic genus Florenciella is quite frequent despite being not one of the most abundant.
In contrast, the abundant small diatom Minidiscus cASV is not present among the 30 most frequent cASVs. The difference in reads abundance and cASV frequency among these two marine phytoplanktonic genera might be a reflection of their coastal-oceanic distribution, which can be easily observed with the online platform of metaPR 2. Florenciella is truly a ubiquitous genus, found in both coastal and oceanic samples, although often in low abundance. In contrast, the nanoplanktonic diatom Minidiscus is mostly found in coastal environments or continental platforms, where it can form sporadic blooms  (Leblanc et al. 2018). Comparing the metaPR 2 metabarcodes to reference databases such as PR 2 , reveals that there are very few novel metabarcodes for supergroups such as Hacrobia and Archaeplastida that contain many photosynthetic taxa.
Global trends across environments The metaPR 2 database corroborates some trends that have been observed in papers with much fewer samples. Singer et al. (2021)  examined patterns of diversity across marine, freshwater and terrestrial (soil) ecosystems based on 122 samples. Using the metaPR 2 database which contain 23 times more samples we are able to establish clear differences across 5 types of ecosystems: marine, coastal, freshwater lakes and rivers and terrestrial (soils). In terrestrial environments, Hacrobia are almost completely absent while Amoebozoa are present but in contrast absent in all the others environments (Figure  6A ). If we use the ecological function, as defined for each major taxonomic group by Sommeria-Klein et al. (  2021 ), the five environments clearly differ by the abundance of parasites, small number of phototrophs and absence of dinoflagellates in soils.
R Shiny application With a database of such size and complexity, it is necessary to create tools that allow in a first step to explore the database and then to download the data of interest (e. g. for a specific taxonomic group or environment). For this purpose, we developed an R Shiny application (Figure  9 ). R Shiny is an open source tool that offers numerous advantage to develop web-based applications in comparison to coding directly under languages such as JavaScript or PHP. It offers predefined components allowing the user to interact with the data (User Interface), while the Server component performs the necessary computations (e. g. filtering, summarizing the data etc. )in the background. Moreover, a Shiny application can be easily deployed on a server using open source tools such as Shiny Server, be packaged in a Docker container that can be downloaded on a personal computer and run locally or delivered as an R package.
The metaPR 2 Shiny application is structured in a number of panels, each dedicated to one type of analysis (e. g. map, diversity). It is possible to select/deselect specific datasets or groups of datasets, such as all oceanic datasets (Figure  S8 ). Selection can also be done based on sample characteristics such as whether samples come from DNA or RNA, the ecosystem, the type of substrate (e. g. ice, water, soil), the size fraction and the depth level (Figure  S9 ). It is possible through reactive menus to navigate the taxonomy tree down to the cASV level (below the species) that potentially corresponding to cryptic or subspecies. cASVs can be filtered based on the number of reads found for this cASV in the whole database (between 100 and 10,000). The number of total reads for a given taxonomic level can be visualized in a treemap (Figure  S10 ).
For continuous variable, bins are created. This panel can also be used for time series with different levels of aggregation (year, month, day). Alpha and beta diversity (Figure  S13 ) can be computed for a limited number of samples (1,000 maximum). It is possible to query the whole set of cASV using a BLAST like query and to map the resulting cASVs (Figure  S14 ). Finally, it is possible to download datasets and samples metadata as well as the cASV and the read abundance for the datasets, samples and taxa selected (Figure  S15 ). The metaPR 2 shiny application besides being very useful for research can also be used for pedagogical purposes. MetaPR 2 can be used as a tool by professors and instructors in the field of microbial ecology. In the framework of the undergraduate course ES2304 -Microbes in Natural Systems at Nanyang Technological University (Singapore), the application was used to investigate the biogeography of several groups of phytoplankton (diatoms, bolidophytes, dinoflagellates, green algae) by groups of 4 students in a flipped-metaPR2 database -p.
11 classroom model. Each group had to do some research on the genus it was assigned and then to analyse the distribution and diversity of key species, answering questions such as whether some species had ubiquitous distributions or controlled by latitude or temperature and whether some species appeared to contain different genotypes as reflected by the presence of several cASVs. In order to make their analysis less daunting, they only analysed the OSD, Malaspina and Tara V4 datasets. Despite the fact that they had only one week to discover the interface and produce their analyses, this hands-on experience resulted in very positive feedbacks by the students, especially regarding using the platform to look at "real-world research data".
Perspectives As its sister database PR 2 which is revised every 6-12 months with the addition of novel sequences as well update in taxonomy, the metaPR 2 will evolve with time to include more datasets and more samples, in particular from ecosystems (e. g. extreme environments), regions (e. g. tropical and southern latitudes) and substrate (microbiomes) that are still little represented. We have tabulated more than 280 metabarcoding studies of protist diversity, for most of which data are available from GenBank SRA. These data will be processed and incorporated into the database with probably yearly releases. The taxonomy of metaPR 2 will evolve in parallel to that of PR 2 and we will add other functional and phenotypic traits (e. g. size, mixotrophy type) as there is clear tendency to use this approach more widely for protists  (Schneider et al.