dataesr/scientific-paragraphs-categorization huggingface dataset:
001). Since this whole dataset suggested that ventilatory activity could have been higher in the low noise condition, we analyzed the respective growth rates by comparing the mean distance between electrodes between the 1 st and last week of the experiment. Despite the low food regime, the animals grew in both situations but less so in standard laboratory conditions. During the whole experimental period, the shell thickness increased by 1. 1 ± 0. 1 mm in the low noise conditions (14. 4 µm/day) but only 0. 6 ± 0. 1 mm under standard noise (8. 1 µm/day; significantly different, p < 0. 001).
Light pollution has emerged as a focus for ecological research in the past few years, and people have mainly focused on ALAN, that is, artificial light at night  (Davies et al. , 2014). The Arabian Gulf has been ranked as one of the most light-polluted areas, as it experiences intensive offshore development and coastal urbanization  (Smyth et al. , 2021). Here, we worked on another type of light pollution, difference of light exposure between two experimental set-up. Although further studies should be developed, our data highlight the importance of taking light intensity into consideration in future studies. Egg laying, corroborated by the presence of juveniles, was observed at two distinct periods, just after the transfer to the laboratory and after a month and a half of observation. On arrival at the laboratory, the clutches were tightly packed in the low noise condition and more spread out in the standard laboratory condition.
This is obviously not natural, and it will have an influence on the animals' behavior, since light parameters are a strong command well known in chronobiology studies  (Davies et al. , 2014; Gaston et al. , 2017). Sound and light pollution are major components of the anthropogenic pressure that we impose on the natural environment. In conclusion, the present dataset confirms the depressant effect of noise pollution added to a light pollution of discreet appearance. It makes it possible to highlight the difference in the level of disturbances, both discreet and profound, that can be observed in contrasting environments as simple as a laboratory room where activity is normal and an unoccupied office. We propose that this gives insight into the impact our seemingly innocuous human activities might have in the wild.
Elasmobranchs (sharks, rays and skates) are among the most threatened marine vertebrates, yet their global functional diversity remains largely unknown. Here, we use a trait dataset of >1000 species to assess elasmobranch functional diversity and compare it against other previously studied biodiversity facets (taxonomic and phylogenetic), to identify species-and spatialconservation priorities. We show that threatened species encompass the full extent of functional space and disproportionately include functionally distinct species. Applying the conservation metric FUSE (Functionally Unique, Specialised, and Endangered) reveals that most top-ranking species differ from the top Evolutionarily Distinct and Globally Endangered (EDGE) list. Spatial analyses further show that elasmobranch functional richness is concentrated along continental shelves and around oceanic islands, with 18 distinguishable hotspots.
Elasmobranch functional diversity We assembled a dataset of 1015 elasmobranch species and assigned seven functional traits as well as their global extinction risk as provided by IUCN Red List statuses  27  to each (see Methods; Fig. S1 ) using primary literature, guides, technical reports, FishBase (www. fishbase. org/) and the IUCN Red List of Threatened Species (www. iucnredlist. org/). Missing trait data and extinction risk (IUCN Red List status = Not Evaluated [NE] and Data Deficient [DD]) were inferred using multiple imputations (see Methods). We performed a Principal Coordinate Analysis to build a three-dimensional trait space for the global elasmobranch assemblage representing 75% of the total inertia (or total variance; Fig. S2 ; Table  S1 ). The first axis of the trait space is strongly related to habitat (i.
E Hotspots of fishing pressures (top 2. 5% of cell values). F Overlap between fishing pressure hotspots and those for the richness of top 2. 5% FUSE and EDGE2 species. All maps have been created using the R environment  51. -180°-135°-90°-45°0°45°90°135°180°- 50°- 25°0 °25°5 0°7 5°A D -180°-135°-90°-45°0°45°90°135°180°- 50°- 25°0 °25°5 0°7 5°B -180°-135°-90°-45°0°45°90°135°180°- 50°- 25°0 °25°5 0°7 5°E -180°-135°-90°-45°0°45°90°135°180°- 50°- 25°0 0°7 5°C -180°-135°-90°-45°0°45°90°135°180°- exposure of these hotspots to anthropogenic threats. Accordingly, we used global databases of fishing pressure (from Global Fishing Watch's Automatic Identification System database of global industrial fishing effort  37  , https://globalfishingwatch. org; Fig. S14 ) and Marine Protected Areas (MPAs; from the World Database on Protected Areas, https:// www.
Elasmobranch data We downloaded a list of elasmobranch species (sharks, rays, and skates excluding chimeras) from Fishbase (www. fishbase. org/; last accessed January 2019) using the R package rfishbase  48. We checked all species names and contrasted them against Weigmann  26  to merge or eliminate names deemed to be synonyms. Further, we assign each species to a family and order based on Naylor et al. 49. Our initial dataset included 1100 species. To each species, we assigned the following seven functional traits: maximum body size (total length or disk width), habitat (coastal, oceanic, or both), terrestriality (marine, brackish, or freshwater), vertical position (benthic, benthopelagic, or pelagic), diet (high vertebrates, fish, invertebrates, plankton or combinations of these), feeding mechanism (macropredators or filter feeders) and thermoregulation (ectothermic or mesothermic; Supplementary Information; Supplementary Data 1).
Trait scores were assigned based mainly on primary literature (see Supplementary Methods) and to a lesser extent, on the species information provided by FishBase  48, 50. We based our selection of traits on Pimiento et al. 18  while tailoring the traitassignments to elasmobranchs. The broad trait categorisations used allowed us to assign trait values to as many species as possible, enabling us to use a global-scale approach. In addition to functional traits, we also assigned species threat status and geographical distribution, both gathered from the IUCN Red List of Threatened Species (www. iucnredlist. org/; last accessed October 2021). Finally, we downloaded the elasmobranch phylogeny (n = 1192 species of sharks, rays, and chimaeras) of Stein et al. 2  , which was constructed using genetic data from 610 species.
Species without DNA sequence were grafted according to a polytomy-resolver algorithm to generate a large distribution of fully resolved phylogenies (n = 10,000). We sampled 100 trees at random to account for phylogenetic uncertainty in the position of species without DNA sequence. We performed all analyses described below using the R environment  51  based on a subset of 1015, which includes only those from which trait, spatial, and phylogenetic data was available, and excludes exclusively freshwater species (see Supplementary Information) and species that occur in grid-cells with less than four species (see below).
Multiple imputations Missing trait data and global extinction risk status (IUCN categories: Not Evaluated [NE] and Data Deficient [DD]) were imputed using the missForest R package, a non-parametric method based on random forests, here set to use 100 random trees  52. Missing data included: habitat (7 spp. ), terrestriality (or freshwater incursions; 1 spp. ), diet (352 spp. ) and size (1 sp); NE or DD IUCN status (170 spp. ). We performed three types of imputations: (1) using known traits and taxonomic information to impute missing traits; (2) using geographical distribution and taxonomy to impute IUCN category; (3) using known traits, geographical distribution, and taxonomic information to impute all missing data. Given the small error (computed as number of incorrect/total number of imputed * 100) found for these three types (error for diet = 12% under imputation type 1 and 7% under type 3; error for IUCN category = 54% under type 2 and 49% under type 3); and the fact that the differences between imputed values using the different methods were minimal, we used the third type to perform all analyses.
We selected the three first axes of the PCoA (Table  S1 ), as they were deemed to provide the best compromise between a low number of axes and the least distortion of the original trait dissimilarity matrix  55. On the basis of the multidimensional trait-space we first quantified the individual contributions of each species to functional diversity using the "fuse" function of the mFD package  56. Specifically, we computed functional specialization (FSp; the distance of each species to the space centroid), functional uniqueness (FUn; the mean distance of each species to its nearest five neighbours); and FUSE (Functionally Unique, Specialized, and Endangered), which is the combination of FSp, FUn, and extinction risk  18  based on extinction probabilities in 100 years, as provided by species' IUCN categories  29, 57.
We used the extinction probabilities as proposed in Mooers et al. 57  and not others recently proposed  58, 59  to ensure a consistent comparison with EDGE2  29. Given that we used modal values across trait imputations to perform our analyses (see above), we explored the uncertainty around FUSE values by computing this metric using each imputed dataset and calculating the mean and standard deviation. As anticipated given high consistency and correlation across imputed data, we found minimal variation around FUSE values (mean standard variation = 0. 00241) suggesting that our ranking of top FUSE species is robust (Fig. S6F ). Then, we computed functional richness (FRic; percentage of convex hull volume occupied in the functional space) per superorder (Selachii and Batoidea), order (n = 12) and global extinction risk status as provided by species' IUCN categories (LC, NT, VU, EN CR; nonthreatened [LC and NT] and threatened [VU, EN and CR]).
Fishing pressure We built a fishing pressure index at 1°resolution from the Global Fishing Watch dataset (https://globalfishingwatch. org; last accessed December 2021) from 2012 to 2020. This dataset consists of standardized apparent fishing effort based on transmissions broadcast using the vessel-tracking system (Automatic Identification System, AIS) originally designed for collision avoidance. Apparent fishing effort is calculated based on fishing hours of all fishing vessels detected in a given area. To compute our fishing effort index, we summed the hours spent at sea of vessels using fishing types known to potentially target elasmobranch species: pole and line; drifting longlines; tuna and other purse seines; trollers; other seines; set gillnets and longlines; and trawlers. It is worth noting that the fishing pressure index used here does not cover the full extent of pressures in the world's oceans given that it only considers regions where the AIS is in place  41.
In addition, this index does not account for illegal and small-scale unreported fishing activities, which represent a major threat for sharks and rays  42. To perform spatial congruence analyses between fishing effort and the different biodiversity metrics, we applied an ordinary kriging technique, whereby a distance of 3°a round each location was chosen for fishing effort interpolation. This distance constraint was utilized to minimize missing values in our fishing pressure index and to align the resolutions of the different datasets (biodiversity metrics and fishing effort).
Marine protected areas and overlap with biodiversity The Marine Protected Area (MPA) database was downloaded from the World Database on Protected Areas (https://www. protectedplanet. net/en, last accessed January 2023). From this first set of MPAs, we retained only MPAs that passed the following sequential filtering criteria (n = 3,376). First, we adopted a conservative approach by selecting MPAs with IUCN categories I to III only (i. e. , Ia, Ib, II, III), which correspond to three management and governance types  65  , and are fully protected marine areas  66. Although commercial or industrial fishing is not allowed in any MPA, we did not consider partially protected marine areas (i. e. , categories IV, V, and VI) because fishing activities are allowed in these areas as long as they are managed and meet MPA objectives.
Acknowledgements C. P. thanks  L. Naidenov  and  D. Spitznagel  for their help accessing some of the data and  C. Jaramillo  for providing valuable insights. This project received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie grant  agreement (no. 663830 ) to C. P. C. P. is now funded by a  PRIMA grant  (no. 185798 ) from the  Swiss National Science Foundation. D. S. received funding from the  Swiss National Science Foundation  ( PCEFP3_187012 ), the  Swedish Research Council  (V. R. :  2019-04739 ), and the  Swedish Foundation for Strategic Environmental Research MISTRA  within the framework of the  research programme   BIOPATH  ( F 2022/1448 ).
Data availability The data used in the study is included in Supplementary Data 1 and 2, and in the "output" files included with the R-codes (see below). The list of species used in this study was downloaded from FishBase (www. fishbase. org). Traits were assigned to species based mainly on primary literature (see Methods and Supplementary Information). Geographical distribution to create the maps (Figs. 2 3 4 ) was downloaded from the IUCN Red List of Threatened Species (www. iucnredlist. org). The elasmobranch phylogeny was accessed from Stein et al. 2. The fishing pressure data shown in Fig. 3e , f was accessed from the Global Fishing Watch (https://globalfishingwatch. org). The Marine Protected Area data used in Fig. 4  was downloaded from the World Database on Protected Areas (https://www.
Statistical analysis Analysis were performed using the SPSS software (v 24. 0, IBM, Chicago, USA). Changes of ADC and FA values were compared using ANOVA for repeated measures with post hoc Bonferroni tests. Interobserver agreements were calculated for ADC and FA using two-way random intraclass coefficient based on R1 and R2 first evaluation and for visual evaluation using unweighted Cohen's κ. Agreements were considered slight (0. 00-0. 20), fair (0. 21-0. 40), moderate (0. 41-0. 60), substantial (0. 61-0. 80), and almost perfect (0. 81-1. 00)  [4]. A p < 0. 05 was retained for statistical significance.
Volatile Organic Compounds (VOCs) have direct influences on air quality and climate. They indeed play a key role in atmospheric chemistry, as precursors of secondary pollutants, such as ozone (O3) and secondary organic aerosols (SOA). To this respect, long-term datasets of in-situ atmospheric measurements are crucial to characterize the variability of atmospheric chemical composition, its sources and trends. The on-going establishment of the Aerosols, Cloud, and Trace gases Research InfraStructure (ACTRIS) allows implementing the collection and provision of such high-quality datasets. In this 15 context, online and continuous measurements of O3, nitrogen oxides (NOX) and aerosols have been carried out since 2012 at the SIRTA observatory, located in the Paris region, France. Within the last decade, VOC measurements have been conducted offline at SIRTA, until the implementation of a real-time monitoring which started in January 2020, using a Proton-Transfer-Reaction Quadrupole Mass-Spectrometer (PTR-Q-MS).
The dataset acquired during the first two years of online VOC measurements provides insights on their seasonal and diurnal 20 variabilities. The additional long-term datasets obtained from co-located measurements (NOX, aerosol physical and chemical properties, meteorological parameters) are used to better characterize the atmospheric conditions and to further interpret the obtain results. Results also include insights on VOC's main sources and the influence of meteorological conditions and air mass origin on their levels, in the Paris region. Due to the COVID-19 pandemic, the year 2020 notably comprised a quasi-total lockdown in France in Spring, and a lighter one in Autumn. Therefore, a focus is made on the impact of these lockdowns on 25 the VOC variability and sources. A change in the behaviour of VOC markers for anthropogenic sources was observed during the first lockdown, reflecting a change in human activities.
Introduction Long-term in situ measurements of atmospheric trace components are crucial to understand their variability, sources, processes, 30 and long-term trends, influencing both air quality and climate  (IPCC, 2021). To this end, the European Aerosol, Clouds, and Trace gases Research InfraStructure (ACTRIS, actris. eu) provides high quality data from in situ and remote sensing measurements. ACTRIS Topical Centers offer technical and scientific expertise such as guidelines and external quality assurance for the set-up of long-term measurements of short-lived atmospheric constituents. An important database containing a large variety of datasets can be found on EBAS portal, including many variables at various European sites (actris. nilu. no). 35 Among the components of interest within ACTRIS, Non-Methane Volatile Organic Compounds (NMVOCs, hereafter referred to as VOCs) are key pollutants, due to their multiple sources and high reactivity in the atmosphere  (IPCC, 2021) , acting as precursors for Secondary Organic Aerosols (SOA) and Ozone (O3) in the troposphere.
, 2016; Bruns et al. , 2017)  -are also considered as key variables for ACTRIS. Here, we present the first long-term PTR-MS measurements in a suburban site in Europe. VOC measurements started in January 2020 at SIRTA, allowing to document and make available, from the ACTRIS data portal, two years of near real-time 80 VOC in situ measurements. The first part of the present manuscript provides details on the instrumental setup, data treatment and quality assurance procedures. Then, a descriptive analysis of the data is proposed. A particular interest is given to the influence of meteorology and air mass origin on the VOCs' loadings, as well as on their seasonal and diurnal variabilities. Finally, a focus is made on the COVID19-induced lockdown period during Spring and Autumn 2020. The SIRTA (Site Instrumental de Recherche par Télédétection Atmosphérique) observation platform is located 20 km southwest of Paris (France) and is considered as representative of suburban background conditions in the Paris region 90  (Haeffelin et al.
Detection limit and uncertainties calculation, ACTRIS quality control The detection limits and uncertainties for each m/z were calculated according to ACTRIS guidelines for PTR-MS (in 220 preparation). Table  1  presents the results for all compounds: the detection limit ranged from 6 to 221 ppt and the uncertainties ranged from 14 to 73%. An internal quality check is performed on all m/z, consisting in carefully verifying the obtained data. An external quality control is also performed by ACTRIS on 12 masses corresponding to the following compounds: benzene, propenal+C4H8, isoprene+furan, C8-aromatics, monoterpenes, toluene, acetonitrile, acetaldehyde, acetone, MEK, methanol and MVK+MACR. 225 Thus, the dataset is compliant with their expectations and can be available on Ebas database. 3 Volatile Organic Compounds phenomenology at SIRTA.
Covid-19 lockdowns To reduce the spread of the coronavirus, a strong lockdown was established in France from March 17 th to May 10 th 2020 included, during which all "non-essential" activities and industries were shut down with a stay-at-home obligation. A second lockdown was established in France from October 30 th to December 15 th , where going to work was possible but restricted and 415 a curfew was set up in the evening and on the weekends. The Spring lockdown period corresponded to the occurrence of unusually high temperatures and sunny days, compared to normal conditions over Europe  (Barré et al. , 2020). Therefore, to quantify a change in pollutants' levels due to the lockdown, the meteorology should be considered  (Gkatzelis et al. , 2021). At the SIRTA site, such a study was done on PM1, BC, NOX, and O3, thanks to the long dataset available  (Petit et al.
Data availability The dataset containing the volume mixing ratios (VMR, in ppb) for all measured mass-to-charge ratios are available on the IPSL Data Catalog (https://data. ipsl. fr/catalog/srv/eng/catalog. search#/home) under https://doi. org/10. 14768/f8c46735-e6c3-45e2-8f6f-26c6d67c4723  (Simon et al, 2022). For compounds that were quality assured by ACTRIS, flags are also given in 475 this dataset, please refer to the associated read-me file. For the figures in the present paper, the data flagged "local contamination/local event" were not considered. The dataset for the compounds that were quality assured by ACTRIS is also available on the EBAS database (https://ebas. nilu. no/); containing the VMR (in ppt), the uncertainties (precision and accuracy, in ppt) and flags, giving indications on the state of the data.
Conclusions In this paper, we provide the first long-term VOC dataset obtained using PTR-MS measurements at a suburban site in Europe. This two-year dataset contains 31 mass-to-charge ratios (m/z) corresponding to 30+ compounds of interest for atmospheric chemistry research, identified thanks to additional PTR-ToF-MS measurements. The SIRTA station is part of ACTRIS, and the data was carefully inspected following quality control and quality assurance procedures before being made available online. 485 The analysis of the dataset conducted here enabled to highlight the specificities of VOCs in a suburban environment. Local sources such as traffic, wood burning and biogenic sources are marked on the compounds' diurnal cycle. The VOC levels are driven here by their sources, meteorological conditions and air mass origin.
Acknowledgements The authors would like to acknowledge  Sébastien Dusanter  and  Marina Jamar  ( IMT Nord Europe, France ) for their help with 525 the data quality evaluation,  Stefan Reimann  and  Matthias Hill  ( EMPA, Switzerland ) for ACTRIS quality control and  Nicolas Pascal  ( Lille Univ. , France ),  Christophe Boitel  ( LMD, France ) and  Sophie Bouffies-Cloché  ( IPSL, France ) for their help with the data submission to the  ACTRIS Data Centre  and IPSL databases. ACTRIS Data Centre  and  ACTRIS CiGas  are acknowledged for their services and support in the QA, QC and data curation. The authors also warmly thank  Nicolas Bonnaire  ( LSCE, France ) for providing NOX and O3 data and  Dominique Baisnee 530  ( LSCE, France ) for the help maintaining measurements during the lockdowns, as well as  Melania Van Hove  and  Simone Kotthaus  from  SIRTA-IPSL  for providing the PAR and MHL data.
Financial support This work has benefited from the support of the research infrastructure ACTRIS-FR, registered on the Roadmap of the  French Ministry of Research. This research has been supported by the  H2020   ACTRIS-2  project (grant no  654109 ) and following 535 related projects. The authors also acknowledge financial support from  CEA  and  CNRS. Leïla Simon  would like to acknowledge the DIM Qi² and  Paris Ile-de-France  region, as well as Ineris for her PhD's fellowship. https://doi. org/10. 5194/essd-2022-406 Preprint. Discussion started: 5 December 2022 c Author(s) 2022. CC  BY 4. 0 License.