dataesr/scientific-paragraphs-categorization huggingface dataset:
The most common example in bioacoustics is the classification of signals by phonetic description (onomatopoeia), such as the "thwop, " "muah, " and "boop" of humpback whale social sounds  (Recalde-Salas et al. , 2020) , and various onomatopoeic descriptions of species-specific and unidentified fish sounds (e. g. ,  Tavolga, 1971; Thorson and Fine, 2002; Staaterman et al. , 2018; Waddell et al. , 2021). Such calls may also be reported in a physics-focused context that include categories of frequency-or amplitude-modulated or continuouswave signals  (Erbe et al. , 2017). Finally, the library and its potential as an AI database would benefit from a scalable design that allows frequent expansion and a web platform that can be continually updated. The database would include a user-friendly interface to investigate data and upload sounds, with automated quality control.
Presentation of the data is also a consideration, not only with respect to the variety of signals, but also individual recording locations and their temporal distributions. Data portals, such as the interactive maps of the North West Atlas in Australia, 11  allow viewers to choose any study site in a map and view a synopsis of species composition and a video snapshot of the site, along with environmental data. This could also be achieved from an acoustic perspective. For example, the data portal of the Integrated Marine Observing System 12  allows viewers to peruse long-term spectrograms of recording sites for a userdefined period. An interactive map that can incorporate all these options becomes a user platform for the acoustic data. OPUS is a recent initiative driven by the International Quiet Ocean Experiment that is currently under development and includes some of these functions.
In an acoustics forum, among other workshops, the special sessions of the Acoustical Society of America [ASA] (2018) identified that there are "an increasing number of applications of machine learning methods in ocean acoustics, particularly when working with large data sets" and discussions focused on data access, code-sharing, and reproducible research. An integrated sound sharing platform begins with three main areas of development to focus and pool efforts: largescale archives of annotated and unannotated audio data, the open-access reference library of identified and unidentified sound sources, and data mining processes, including AI algorithms. Acoustic repositories and data portals, such as OPUS and IMOS, are becoming increasingly common. Importantly, initiatives such as the allocation of 15 petabytes for a passive acoustic data portal by the National Center for Environmental Information (NCEI) of the National Oceanic and Atmospheric Administration (NOAA; United States of America) illustrate the growing appreciation and realization of this need at the national scale  (Wall et al.
, 2021). Reference libraries have existed on various scales for many years and advances in technology are quickly increasing their ability to expand and integrate user contributions. The Detection Classification, Localization and Density Estimation (DCLDE) and the Detection and Classification of Acoustic Scenes and Events (DCASE) workshop series  (2003-2022 and 2013-2022, respectively)  have focused on data mining and analytical approaches. These groups have been the main producers of public datasets to advance machine learning applications of biological sounds in the ocean  (Frazao et al. , 2019)  and the workshops regularly provide training sets to test detection algorithms under different conditions (e. g. , various frequency-dependent SNR and propagation losses). 13 Their outputs have shown what is achievable from data-sharing of comparatively "small" platforms (previously up to 10 TB), which complement the sharing of open-source code that individuals are increasingly providing with the publication of analytical works (e.
g. ,  Bergler et al. , 2019; Bermant et al. , 2019; Madhusudhana et al. , 2020; Lin et al. , 2021). Together these activities highlight the potential for applications of data-sharing of acoustic information to be applied to larger repositories that are now more achievable with cloud-based options, such as AI for Social Good, 14  or government supported platforms, such as the NCEI. Although the development of a global integrated and openaccess underwater sound reference library, repository and sharing platform has been suggested previously, despite these discussions and the increasing appearance and support for individual components of such a program, it has not been fully realized on an international level. The main barrier to creating an international database of aquatic bioacoustics may be as simple as sourcing adequate funding to achieve such a sizable task, due to a lack of awareness of the value and importance of the product among organizations with the financial resources to support its creation and continuation.
This could be alleviated by integrating efforts from existing libraries and archives, rather than initiating an entirely new database, which will also increase the library's appeal to potential funding sources. There may also be more nebulous factors that have limited the provision of appropriate funding, including the likely duration of the program (i. e. , including long-term planning and on-going resources to maintain the platform) and facilitating the repeated meeting of numerous global partners needed to identify and agree on its structure and criteria. Securing the longevity of the program is vital to the usefulness of the platform as libraries that are not scalable, well-maintained, and continually updated can quickly become redundant or outdated. The world's increasing awareness around the environmental costs of data storage and processing mean that consideration of carbon neutrality will also be a key factor in the design and longevity of the program.
Passive acoustic research is now, it appears, rapidly approaching a nexus point. The changing environment and decreasing biodiversity are compelling the documentation of baseline acoustic observations. Technical advances associated with data collection and an increasing number of researchers and institutes collecting PAM data are providing the ability to create bioacoustic databases. Concurrently, awareness of the importance of acoustic cues to aquatic fauna, the impacts of noise on them and the potential for acoustic communities to provide an indication of ecosystem health has reached a stage where PAM is becoming appreciated as a mainstream data source across more species and ecosystems than ever. Finally, public interest and access to user applications means citizen scientists can drive widespread knowledge sharing.
These are multidisciplinary tasks requiring input from bioand eco-acousticians, bioinformatics experts, AI engineers, web engineers, and stakeholders. To begin our journey along this shared pathway, we recommend a multi-disciplinary workshop to detail all the requirements for developing an appropriate library/database to fulfill the needs of all that may wish to access it and to detail the resources needed to support the work. Such an effort is critical and timely as we enter the UN Decade of Ocean Science for Sustainable Development.
Acknowledgments We wish to thank two anonymous reviewers for their thorough and helpful comments. The  Ar/Ar facility at ISTO  is supported by the  LABEX  project  VOLTAIRE  ( ANR-10-LABX-100-01 ), the  Région Centre  project  ARGON , and the project  EQUIPEX   PLANEX  ( ANR-11-EQPX-0036 ). RW acknowledges  Natural Environment Research Council   studentship grant   NER/S/A/2006/14156. NJJ gratefully acknowledges funding from the  German Academic Exchange Service, Geological Society of London, Mineralogical Society of Great Britain and Ireland ,  Geologists Association, Quaternary Research Association, Volcanic and Magmatic Studies Group  and the  Department of Geology at the University of Leicester.
Moreover, benthic foraminiferal Ba/Ca has mainly been used in open ocean records as a proxy for paleo-productivity. Here we report on a new geochemical dataset measured from living (CTG-labelled) benthic foraminiferal species to investigate the capability of benthic Ba/Ca to record changes in river runoff over a gradient of contrasting hydroclimatic conditions. Individual foraminifera (Bulimina marginata, Nonionellina labradorica) were analyzed by laser-ablation ICP-MS over a seasonal and spatial gradient within Gullmar Fjord, Swedish west coast during 2018-2019. The results are compared to an extensive meteorological and hydrological data set, as well as sediment and pore-water geochemistry. Benthic foraminiferal Ba/Ca correlates significantly to riverine runoff, however, the signals contain both spatial trends with distance to Ba-source, and species-specific influences such as micro-habitat preferences.
The LA-ICP-MS was operated in manual mode for sample analyses. Elemental baseline levels before each spot analysis were measured for at least 30 sec. S1 ), and to correct for instrumental drift. Primary standard NIST 610 and secondary standards, NIST612,  MACS-3 (MicroAnalytical Carbonate Standard, United States Geological Survey, 2012; Jochum et al. , 2012) , JCt-1 and JCp-1 (AIST Japan calcium carbonate pellets) were run at the beginning and end of each session and the data used to monitor the quality of the analyses (Table  S1 ). A selection of these -NIST 610, MACS-3, JCp-1 -were additionally run after each 10-15 sample spots. For each specimen, the final (n), penultimate (n-1), and antepenultimate (n-2) chambers (as far as possible), were targeted and ablated from the outside to inside of the wall.
Benthic foraminiferal trace-element data processing Raw counts were converted to elemental concentrations (ppm) with the software package Igor Pro 6. 37 with Iolite v3. 5  (Paton et al. , 2011). The element ratios were calculated based on the average concentration measured during the ablation period of the primary test calcium carbonate. This signal interval was determined through manual examination of each trace element profile. Constant counts of 43 Ca were used to find integration intervals of calcite ablation. Intervals of enriched signals of 27 Al, 55 Mn and/or 66 Zn, indicative of secondary surface coatings and/or contaminations, were excluded. Such were typically measured at the beginning and/or end of each profile (corresponding to the outer and inner test wall surfaces, respectively), with relatively constant raw elemental counts intermediate (see also e.
g. ,  Ni et al. , 2020). Trace elemental ratios (TE/Ca) were calculated with the assumption of the TEs being bound as carbonate (assuming 40% wt Ca in the CaCO3 matrix) and normalized to calcium. 3 ) application of a 95% confidence limit on Ba/Ca, for each species, site and season, to remove outliers (i. e. , > 2 * standard deviation) (39 signals). In total, 78 of 832 (9. 4 %) ablation profiles were rejected from further analyses. From the benthic foraminiferal Ba/Ca values the median and MAD was calculated per species, season, and site, to limit the influence of extreme outliers. As no systematic differences between sediment intervals were observed, Ba/Ca data were pooled (Welch ANOVA, Table  S2 ). If not indicated otherwise, only data on n-chambers were considered, assuming these were precipitated in close temporal proximity to the time of sampling.
Meteorological and hydrological data All meteorological, hydrographical, and hydrological data of the Swedish west coast, the sill fjord, and its catchment area are publicly available from SMHI (http://www. smhi. se). We used the SHARK hydrographic database (https://sharkweb. smhi. For both hydrological and meteorological data, the averages were calculated for 30-, 60-, 90 and 120-day periods preceding the sampling occasions directly or by one month, respectively (i. e. , four periods ending on and four period ending one month prior to the sampling dates).
se) to obtain monthly data on salinity, [O2], and/or chlorophyll-a across 2018 and 2019 from three fjord stations Släggö (58º25. 98'N, 11º43. 57'E, 70 mbsl), Alsbäck (58º19. 40'N, 11º32. 80'E, 120 mbsl) and Björkholmen (58º23. 26'N, 11º37. 60'E, 50 mbsl) (Fig. 1), of which Alsbäck is closely located to GF 117. Daily runoff and sediment load data were collected from the hydrological database 'vattenweb' (http://vattenweb. smhi. se/, last access: February 2021), partly based on the S-HYPE (Hydrological Predictions for the Environment) model maintained by SMHI.
Acknowledgements The authors thank the Captain and crew of the RV Oscar von Sydow and  RV Skagerak  for technical assistance. We thank  Sami Jokinen  and  Hanna Nilsson  for their help during the cruises. We acknowledge the staff of the  Kristineberg Marine Research Station  for their support during the field campaigns. This work was supported by the  Swedish Research Council VR  [grant number  2017-04190 ]; the  Crafoord Foundation ; and the  Royal Physiographic Society in Lund, Sweden. Tom Jilbert  acknowledges funding from the  Academy of Finland  (grants  317684  and  319956 ). The SHARK data collection is organized by the  Swedish environmental monitoring program  and funded by the  Swedish Agency for Marine and Water Management (SwAM).
Wave climate and tides Datasets used to characterize wave climate and tidal regime in the study area include wave data obtained from the coupling of wind and numerical wave models, as well as seawater level information, recorded using a tidal gauge, and have been provided by the Spanish Port Authority (Puertos del Estado). The reanalysis database containing wave information through wind and numerical wave models is called SIMAR and provides information covering 64 years, from January 1958 to March 2022. The location of the selected computational grid node is indicated in Fig. 7. This node is located close to Montaña del Tesoro and exposed to wave conditions similar to those reaching the study area. In particular, the wave data set includes hourly values of spectral significant wave height, H m0 , peak period, T p , and mean wave direction, D m.
Acknowledgments Financial support was provided by Project LAJIAL (ref. PGC2018-101027-B-I00 ,  MCIU/AEI/FEDER , EU). CPT acknowledges the PhD grant  2021 FISDU 00347  funded by the  Departament de Recerca i Universitats de la Generalitat de Catalunya. This study was carried out in the Research Consolidated Groups GEOVOL ( Canary Islands Government, ULPGC)  and  GEOPAM (Generalitat de Catalunya ,  2017 SGR 1494 ). We acknowledge the support of  D. Fernández  ( GEO3BCN ) on the reference management. We thank the editor  D. Kennedy , and  R. Ramalho  and an anonymous reviewer for their helpful reviews.
Alignment and genotyping Ambiguous, low quality reads, sequencing adapters and spliced-leader sequences were trimmed from raw reads using Trimmomatic version 0. 33  [68]  with parameters ILLUMINACLIP: Adapters. fasta : 2:30:10, LEADING : 3, TRAILING : 3, MAXINFO : 80:0. 8, and MINLEN : 70. Trimmed reads were aligned to a reference transcriptome developed previously  [64]  using BWA-MEM (Burrows-Wheeler Alignment Tool  [69] ). Only reads with a mapping score >10 were retained. Pairs for which the two reads did not map concordantly on the same transcript were removed. Samtools was used to sort and index bam files, as well as to generate a raw count table for gene expression analysis  [70]. Freebayes  [71]  was used to genotype the strains using 457 368 SNPs previously validated  [64]. Freebayes was first run enforcing diploidy.
Annotation The reference transcriptome was initially annotated based on homology with the UNIPROT/SWISSPROT database  [64]. To improve the annotation of sxt genes, a tblastn  [73]  analysis was performed against the reference transcriptome using as queries sxt genes from dinoflagellates and cyanobacteria extracted from GenBank (sxt A to Z and sxtSUL, DIOX, PER, ACT) (e-value <1e -10 ). An additional blastx was performed with the transcripts identified by tblastn against the nr database (evalue <1e -5 ) with these candidate transcripts. TransDecoder version 5. 5. 0  [74]  was used on the transcriptome to select the longest ORFs and generate the associated protein sequences. These sequences were searched against the HMM library pfam-A. hmm  [75]  using the hmmsearch function of the HMMER programme (version 3.
Genetic linkage map Transcripts presenting genetic variability (polymorphic at one or several SNP positions) among the four parents were considered to develop a genetic linkage map (38 690 transcripts). Transcripts presenting multiple recombination events in the F1 strains, possibly reflecting chimeric transcripts (1798 transcripts), as well as transcripts displaying missing data in at least one strain (23 172 transcripts) were removed from the initial dataset. The remaining 13 720 transcripts were used to develop a genetic linkage map using the mstmap function of the R package Asmap  [77] [78] [79] , with default parameters except for bychr parameter (set to 'false' , no preconception about the partition of transcripts in different chromosomes) and a p-value threshold set to 1e -7.
Raw read counts of all F1 RNA-Seq libraries were normalized using the vsd function of the Deseq2 package  [80]. Still focusing on the T2xNT2 cross, these expression data were used to perform the eQTL analysis on the 31 transcripts of the gene cluster linked to toxicity (see Results) using the R package MatrixEQTL  [81]. This analysis could indirectly identify genes underlying the ability to produce toxins due to a presence/ absence pattern, as such genes would not be expressed if absent from the genome of a given strain.
Acknowledgements The authors acknowledge  Pascale Malestroit  for preliminary work that lead to the obtention of the F1 strains,  Laure Guillou  for initially providing the parental strains,  Sébastien Artigaud  for insightful comments, the  Pôle de Calcul et de Données Marines (PCDM)  for access to the DATARMOR calculator, the Sebimer team for bioinformatic assistance, the  GetPlage sequencing facility  for sequencing, and the reviewers for their insightful comments.
Acknowledgements. The authors would like to thank the  French Polar Institute (IPEV, Institut polaire français Paul-Émile Victor)  which supports the science programme 209 operations on the  Dumont d'Urville station , as well as the different operators of the  DDU  lidar who conduct measurements all year long in Antarctica and are essential to this work. Financial support. This research has been supported by the  French Polar Institute IPEV  ( Institut polaire français Paul-Emile Victor) science programme 209 , the  EECLAT  project ( CNES-INSU ), and the  NDACC international network  (lidar instrument). Review statement. This paper was edited by  Matthias Tesche  and reviewed by three anonymous referees.
Data availability. The Dumont d'Urville lidar instrument is part of the NDACC international network and data are publicly available online at the NDACC/NOAA data archive (https://www-air. larc. nasa. gov/missions/ndacc/, last access: 29 December 2022;  Jumelet, 2022). ERA5 reanalysis data are available on the Copernicus Climate Data Store at https://doi. org/10. 24381/cds. bd0915c6 (Hersbach et al. , 2018). NCEP/NCAR reanalysis data are available at https://www-air. larc. nasa. gov/missions/ndacc/data. html?NCEP= ncep-list (last access: 11 January 2023; NCEP/NCAR, 2023;  Kalnay et al. , 1996; Jumelet, 2022).
The Biomass Burning Emission The biomass burning emission fluxes are diagnosed from the CAMS data set  (Kaiser et al. , 2012). These fluxes are proposed as surface fluxes with a 0. 1 × 0. 1° horizontal resolution. Then, they are remapped on the CHIMERE horizontal grid, aggregated into the model species and vertically redistributed. The fluxes are also converted from daily to hourly bases using a diurnal profile. Vertically, the surface flux redistribution is a key point. First the injection height, H p , is diagnosed following the schemes of  Sofiev et al. (2012)  and  Veira et al. (2015). The calculation used the Convective Available Potential Energy estimation being diagnosed using the Fire Radiative Power (FRP) for each fire, as: 𝐻𝐻𝑝𝑝 = 𝛼𝛼𝐻𝐻𝑎𝑎𝑎𝑎𝑎𝑎 + 𝛽𝛽 ( 𝑃𝑃𝑓𝑓 𝑃𝑃𝑓𝑓0 ) 𝛾𝛾 exp ( - 𝛿𝛿𝛿𝛿 2 𝐹𝐹 𝐹𝐹 𝛿𝛿 2 0 ) (1) with α = 0.
Change in Soil and Surface Characteristics The soil and surface change is an important point for this study, because a fire may induce a more erodible nature. This change is also relatively unknown at the regional scale and several hypothesis have to be made. The first hypothesis regards the surface of the burnt area. This information is not provided with the data used. Some products exist with other biomass burning models, but these data are still uncertain. In addition, the CAMS data are recognized to have accurate results on the location and intensity of fire fluxes and there is a strong interest to use it in the framework of the WRF-CHIMERE system when used in the Copernicus CAMS daily forecast. For this study, the hypothesis is made that the burnt surface should be proportional to the fire radiative power via the fire size.
The Test Case and the Observation Data The test case corresponds to the summer of 2021, from 1 July to 31 August and for the whole Europe and a part of Asia. During this period, numerous fires were observed as described by the European Forest Fire Information System (EFFIS) web site with daily biomass burning maps (https://effis. jrc. ec. europa. eu). Two main spots are observed: in Russia, as already studied by  Konovalov et al. (2011) ,  Konovalov et al. (2015) , and in Greece as already studied by  Hodnebrog et al. (2012) ,  Rea et al. (2015) , and among many others. The simulation domain is presented in   MENUT ET AL. 10. 1029/2022JD037395 7 of 21 • "DFvert": Same configuration as "DFno" but with the impact of fires on mineral dust emissions. For the impact at the surface, the simulation results will be compared to particulate matter, PM 2.
5 and PM 10 surface concentrations. Mineral dust having also an impact on radiative transfer, then photochemistry, comparisons are also performed with surface ozone concentrations. All measurements are hourly and come from the European Environment Agency (EEA, https://www. eea. europa. eu) database. Stations are presented in Figure  8. For the impact on the whole troposphere, we compare the simulation results to aerosol optical depth (AOD) data taken from the AErosol RObotic NETwork global remote sensing network (AERONET, https://aeronet. gsfc. nasa. gov), (Holben et al. , 2001)  and the inversion product providing aerosol size distribution  (Dubovik & King, 2000). For the vertical structure of the troposphere with aerosol, the CALIOP lidar is used. It is on-board the Cloud-Aerosol Lidar Pathfinder Satellite Observation (CALIPSO) satellite  (Winker et al.
Time-Series of Aerosol Optical Depth and Angström Exponent Time series are presented in Figure  11  for AOD and ANG. Data are from the AERONET database v. 1. 5 (Figure  8 ) and are averaged daily same as the model results. Athens (Greece), IASBS (Iran) and Dushanbe (Tajikistan) stations are selected because an effect is visible and they correspond to the available data closest to the fire areas. For many other stations, no change (or negligible changes) were observed. For all locations, AOD is under-estimated by the model, independently of the configuration DFno or DFvert. Despite this bias, the time variability is realistic for a major part of the stations, except Dushanbe. The differences between DFno and DFvert are low in Athens but non negligible in IASBS and Dushanbe. For ANG, the comparison shows that the variability of the aerosol size distribution is well reproduced by the model.