dataesr/scientific-paragraphs-categorization huggingface dataset:
Methodology The initial concept was to install the Vaisala PTU and Anasphere SLW sensors on board the drone, the SLW sensor being connected to the PTU sonde, with a real time data transfer to the Vaisala ground station. Two phases were defined for flying with the drone corresponding to (1) technology flights (no sondes on board) and (  2 ) science flights (sondes on board). Although this was a risky project, we were able to use a drone in this challenging environment and provide scientific results. Thus, 13 flights were conducted from 24 December 2022 to 17 January 2023: 9 technology flights and 4 science flights (Table  2 ). We have mainly concentrated our study on the four days when scientific flights were successfully performed, namely on 2, 10, 11 and 13 January 2023. Information regarding the speeds (maximum horizontal, up and down speeds, ground speed, height rate and average forward speed) of the drone during the 4 fligths is presented Table  3.
Data Availability Statement: HAMSTRAD data are available at: http://www. cnrm. meteo. fr/spip. php?article961&lang=en (last access: 9 August 2023). Radiosondes are available at http://www. climantartide. it (last access: 9 August 2023). Lidar data are available at http://lidarmax. altervista. org/lidar/Antarctic%20LIDAR. php (last access: 9 August 2023).
Acknowledgments: The present research project Water Budget over Dome C (H2O-DC) has been approved by the Year of Polar Prediction (YOPP) international committee. The permanently manned Concordia station is jointly operated by IPEV and the Italian Programma Nazionale Ricerche in Antartide (PNRA). The tropospheric LIDAR has operated at Dome C since 2008 within the framework of several Italian national (PNRA) projects. We would like to thank all the winterover personnel who worked at Dome C on the different projects: HAMSTRAD, operational meteorological soundings, in situ balloon-borne and drone-based observations, and the aerosol LIDAR. Special thanks to Armand Patoir (IPEV) for his valuable work on the drone repairs. Finally, we would like to thank the three anonymous reviewers for their beneficial comments.
As part of the HIPPO (HIgh-resolution Primary Production multi-prOxy archives) project, environmental monitoring was carried out between March and October 2021 in the Bay of Brest. The aim of this survey was to better understand the processes which drive the incorporation of chemical elements into scallop shells and their links with phytoplankton dynamics. For this purpose, biological samples (scallops and phytoplankton) as well as water samples were collected in order to analyze various environmental parameters (element chemical properties, nutrients, chlorophyll a, etc. ). Given the large number of parameters that were measured, only the major results are presented and discussed here. However, the whole dataset, which has been made available, is much larger and can potentially be very useful for other scientists performing sclerochronological investigations, studying biogeochemical cycles or conducting various ecological research projects.
Lanvéoc is character- ized by shallow waters (from approx. 8 to 15 m depending on the tide, with a mean depth of approx. 11. 5 m recorded at mid-tide) and a seafloor made up of sandy and silty sediment, with significant amounts of large-sized biogenic detritus (shells, maerl). Water samples from the surface (1 m deep) and bottom (approx. 20 cm above the sediment) were collected twice a week between 16 March and the end of June 2021 and once a week thereafter until mid-October 2021. No sampling was conducted during August, leading to a lack of data for this month. Several biological, chemical and physical parameters were measured in these seawater samples; an exhaustive list is given in Table  1 , which can be found at the end of this article. For samples dedicated to trace element measurement, a 5 L Teflon-coated GO-FLO sampling bottle was used.
Organic matter analyses Seawater samples dedicated to chlorophyll a (Chl), pheophytin (Pheo), particulate organic carbon and nitrogen (POC and PON, respectively) and biogenic silica (BSi) analyses were collected from the surface and bottom waters using a 5 L or a 8 L Niskin bottle. Some samples for the bottom layer were collected using a syringe for a few dates (collecting strategy is specified in the dataset). Water samples were filtered through glass fiber filters (GF/F, Whatman) and pre-combusted for 4 h in a muffle furnace at 450 • C for Chl, Pheo, POC and PON measurements. BSi samples were analyzed after filtering seawater through 0. 6 µm polycarbonate (Merck) filters. For each parameter, the filters were stored in an appropriate setting, i. e. , POC/PON and BSi samples in a dark and dry place, and Chl and Pheo samples were kept at -80 • C.
Since the salt matrix of seawater samples can affect chemical measurements, samples were diluted 100 times with 2 % HNO 3 prior to the measurement. All particulate and dissolved concentrations shown in the present study were above detection limits. The data obtained by ICP-MS and ICP-OES were also corrected for machine drift by inserting multi-element standard solutions every two samples. A certified reference material, NASS-6 seawater (National Research Council, Canada), and a standard solution prepared from a multi-element solution (1 ppb; VWR Chemicals and Prolabo) were used in order to assess the accuracy of measurements in the dissolved and the particulate phase, respectively. The network graphs show the Pearson correlations between all the trace elements measured in the particulate fraction of the surface and bottom water samples obtaining during HIPPO monitoring (Fig.
6 µm to 26 mm, divided into 25 size classes). The data are available from the ECOTAXA server (https: //ecotaxa. obs-vlfr. fr, last access: 24 July 2023). Secondly, the UVP6 took full high-resolution images every 2 s for 3 min to calculate particle trajectories and settling velocities. Due to the colossal size of these images, they are not available on SEANOE but are available upon request. Figure  7  shows the temporal variation of small particles (daily sum of the mean concentration of particles ranging from 80. 6 to 512 µm in size) and large particles (daily sum of the mean concentration of particles ranging from 512 µm to 26 mm in size). Several aggregation periods were detected by isolating peaks in large particle concentrations whose amplitudes were at least twice as large as the minimum values at the base of each peak.
Sediment trap data A HYDRO-BIOS Multi Sediment Trap with 12 collecting bottles (total volume: 290 mL) was deployed during the survey to analyze the chemical composition of the particles that sedimented at the seafloor. The catchment area of this model was 153. 86 cm 2 with a rotation frequency of every 3 d (between 2 March and 25 May) and every 4 d (between 26 May and 29 July). Before deployment, each bottle was poisoned to prevent biological activity and thus degradation of the organic matter. Sedimenting material was analyzed for the trace element composition, as well as POC, PON and BSi. Protocols used for these measurements are described in Sect. 3. 3. 2 and 3. 4. Before filtration, the samples were shaken (to break up the aggregates) and passed through a 1 mm mesh sieve to remove big particles and avoid clogging the filters.
Conclusions In this article, only an overview of the results gathered during the HIPPO monitoring conducted at Lanvéoc during 2021 is presented. The dataset helps in better understanding the links between phytoplankton dynamics, water column chemistry and the incorporation of trace elements into the shells of P. maximus. However, the dataset also contains information useful for other topics of interest. Tables  1  and 2  compile all variables that have been made available for other scientists on the SEANOE platform (https://doi. org/10. 17882/92043 -  Siebert et al. , 2023). Moreover, the hypotheses and assumptions given in this paper, as well as other topics that have not been mentioned, will be the focus of several articles that are currently in preparation. Author contributions. The HIPPO monitoring was initially designed by JT and BRS, the leader of the project.
Trace element measurement was performed by VS, accompanied by MW in sample preparation, and by MLR, YG and CL in the ICP-MS and ICP-OES analysis sessions. LF prepared the shells for LA-ICP-MS measurement, performed the measurement with KPJ and processed the data. LF also acquired the daily growth rate data on the P. maximus specimens. The deployment of the UVP6 was made possible thanks to the work of MP, who designed the instrument and kindly lent it to the team for this monitoring activity. MP also helped process the data alongside CM. PRM prepared and set up the Sambat/NKE probes and discharged and processed the data. Finally, VS gathered all the data that were obtained for all parameters. VS led the writing of the article. All authors contributed critically to the drafts and gave final approval for publication.
Acknowledgements. We warmly thank  Brigitte Stoll  from the  Max Planck Institute (Mainz)  for help with the LA-ICP-MS analyses. We are also very grateful to the crew of the Albert Lucas for their involvement in this environmental monitoring activity and their support during the cruises. Moreover, we wish to thank the Plateau d'analyse chimique des paramètres de base de l'environnement marin (PACHIDERM) as well as the  Pôle Spectrométrie Océan (PSO)  for the measurements of the various chemical parameters. This survey has been performed within the framework of the  French-German collaborative project HIPPO (HIgh-resolution Primary Production multi-prOxy archives ). Financial support. This research has been supported by the  Agence Nationale de la Recherche  (grant no. ANR-18-CE92-0036-01 ), the  Deutsche Forschungsgemeinschaft  (grant no.
INTRODUCTION High throughput biotechnologies and analytic approaches applied to large human cohorts have recently revolutionized biomedical research, allowing the quantification and characterization of biological molecules to generate ''omics'' datasets. Genomics, the characterization of an individual's DNA molecules, led to the identification of thousands of genetic variants associated with a trait, disease, or response to treatments, through large-scale genome-wide association studies (GWAS). 1  Although GWAS have resulted in a better understanding of disease mechanisms, these approaches only consider genetic variation established at birth, and ignore the environment of an individual, influencing its biological state. An alternative strategy to complement traditional GWAS and better understand human biology is to comprehensively interrogate disease states at the molecular level using metabolomics, which offers a robust way to systematically measure thousands of low-molecular-weight compounds, called metabolites.
After tissue extraction or collection of biological samples (usually blood and urine), metabolites can be detected, identified, and quantified using either mass spectrometry (MS) or nuclear magnetic resonance (NMR). 2  As biomarkers of the underlying molecular dysfunctions, metabolite levels correspond to intermediate phenotypes (or endophenotypes) representing natural or clinical heterogeneity. Storing and sharing metabolomics knowledge is the focus of the Human Metabolome Database (HMDB), which is one of the largest and comprehensive curated collection of human metabolite and human metabolism data in the world. 3  Metabolomics can be seen as the study of the ultimate molecular response of an organism to genetic, environmental, and pathological modifications, but elucidating specific molecular mechanisms from metabolomics alone is difficult since many metabolites are involved in multiple biological processes.
11, 12  Only few methods have been developed to gain insight into the biological interpretation of mGWAS data specifically,  13, 14  and none have been explicitly used to annotate mGWAS results based on well-curated biochemical knowledge, such as the Kyoto Encyclopedia of Genes and Genomes (KEGG) database. 15  KEGG is recognized for its high curation level of metabolic pathways for many model organisms, including humans, and is a reference for the reporting of enzymatic reactions, which is needed to understand the functional importance of gene-metabolite pairs. KEGG is a reference for the biochemical functions of genes with the descriptions of enzyme reactions, but a systematic and quantitative annotation procedure using this database in the context of mGWAS results remains to be explored.
In this regard, recent studies have highlighted the applicability and utility of topological data analysis based on graph theory approaches,  16  including the shortest path,  17, 18  in revealing biological knowledge in the context of complex metabolic networks. 13, 17, 18  For example, the shortest path was used to characterize the impact of gene deletion on nearby metabolites within the metabolic network of E. coli. 18  It was also used to analyze the relationship between expression quantitative trait loci (eQTL) and metabolomic data, for example within the metabolic network of rat adipose tissues. 17  Still, this type of metric has not been applied to systematically annotate mGWAS results. In this study, we assessed the utility of the shortest reactional distance (SRD) metric computed from KEGG database's pathways to annotate mGWAS results and to help extract biological insights from them.
We developed PathQuant, an R package, to enable a robust and systematic computation of SRD values between any lists of gene-metabolite pairs mapped onto KEGG graphs, which represent metabolic pathways, while keeping the original, well-curated, topology of each queried pathway. Focusing on genes encoding for enzymes and their associated metabolites, we applied the SRD annotation to two previously published mGWAS datasets in individuals from different ethnicities: an mGWAS 11 performed on 7,824 participants from the TwinsUK cohort  19  and the Kooperative Gesundheitsforschung in der Region Augsburg (KORA) study,  20  referred herein as the TK study, and an mGWAS  21  performed on 614 Qatari participants from the Hamad Medical Corporation (HMC), referred herein as the HMC study. We explored results at varying levels of statistical significance.
Third, SNPs are mapped to their closest genes coding for an enzyme (within 10 kb upstream and downstream) to obtain gene-metabolite pairs. As multiple SNPs will generally be linked with the same gene, only the minimum p value of all SNPs is kept for a gene-metabolite pair, representing the strongest statistical signal for each pair within a study. These p values can be categorized as significant, suggestive, or non-significant according to appropriate thresholds that depend on the dataset (STAR Methods). We then retrieve KEGG IDs from all pairs (STAR Methods), and only retain pairs for which both IDs could be found. Third, we run PathQuant R package to compute the SRD value on all remaining pairs based on the KEGG overview graph (hsa01100). Note that other KEGG graph, or a combination of KEGG graphs, can be easily added at this step.
Stringent and suggestive associated pairs have shorter reactional distances To test whether the SRD is a metric capable of capturing the biological relevance of a gene-metabolite pairs, we first explored the results of the TK study, as it represents one of the largest mGWAS studies conducted to date. This mGWAS was done on a group of participants from European descent, and many findings were replicated in follow-up studies, making it a well-validated dataset. Furthermore, it is considered a reference study by the community, resulting in an ideal dataset to test the hypothesis that stringently associated pairs will have lower SRD values. We started to explore this dataset by using the 74 gene-metabolite pairs with KEGG IDs passing the genome-wide significance cut-off set by the original authors  11  (Table  1 ).
Interestingly, within the 40 associations with an SRD annotation, some genes are closely connected to multiple metabolites, such as ACADM and CPS1 that have multiple small SRD values. Moreover, some genes are isolated and appear to be part of disconnected subgraphs of the KEGG overview graph, such as GMPR (guanosine monophosphate reductase), NAT2 (N-acetyltransferase 2) and TDO2 (tryptophan 2,3-dioxygenase), which obtained infinite SRD values with most metabolites. The most widely used approach to report mGWAS gene-metabolite pairs is based on genomic distances, but identifying the potential causal gene behind an SNP association is highly challenging. We evaluated the validity of the distance criteria based on SRD. Using the TK dataset, we evaluated the number of additional gene-metabolite pairs and the SRD distribution of pairs when increasing the interval size (10 kb as baseline, 50 kb, 500 kb, 1 Mb) to map the gene (STAR Methods, Figure  S3 ).
At an mGWAS p value below 1 3 10 À7 , the On the left panel, we show the main steps of the pipeline used to annotate mGWAS datasets. Boxes indicate input and outputs of processing steps described on arrows. The right panel shows the meaning of an SRD annotation of Enzyme A's pairs within a KEGG pathway, where genes are represented as edges and metabolites as nodes. The SRD between Enzyme A and its main reactants, the substrate and the product, are of 0, everything running deeper adds 1 at each step (dashed arrows): green metabolites are at SRD = 1 and the blue metabolite is at SRD = 2 from Enzyme A. 10 kb baseline yielded a total of 76 pairs, and extended intervals incrementally introduced 8, 105, and 70 new pairs for 50 kb, 500 kb, and 1 Mb, respectively. The mean SRD for the 10 kb baseline pairs is 2.
We identified a total of eight loci associated with metabolite levels (Figure  S6 ), with five of them reported in previous studies (Table  S2 , containing significant associations from the SCD study). The three additional associations were not previously reported neither in the TK/HMC studies, nor in a large mGWAS of human blood metabolites. 34  In all three cases, the frequency of the top associated SNP is larger in individuals of African descent than in Europeans according to the gnomAD Genomes database, but these hits were not found in the four largest mGWAS done in individuals of African ancestry to date. [35] [36] [37] [38]  We recognize that while interesting, these novel associations will need replication in an independent cohort. We generated a QQplot based on minimum p values for the gene-metabolite pairs extracted from the SCD mGWAS (STAR Methods) and defined the gene-metabolite significance cut-off at p < 3.
The datasets we used in our study involved different genomics and metabolomics protocols, different preprocessing strategies, different sets of genetic variants (genotyped, imputed, and exclusive to exons) and of metabolites, different ethnicities (from European, African, and Middle Eastern ancestries), disease-based and population cohorts, illustrating the wide range of contexts in which our annotation is applicable. PathQuant, the package developed to compute the SRD metric in this study, is not the only package available that computes the shortest path metric between genes and metabolites. Similar to PathQuant, MetaboSignal  13  is an R package based on the same mathematical criterion of shortest path metric and also uses metabolic pathways from the KEGG database. There are, however, important differences between these two methods.
Despite the number of investigated associations being very small for the R and S+ categories, we observed a decreasing proportion of pairs with values lower than 8 and an increasing frequency of pairs with values greater than 8, as p values increases, in line with the negative correlation observed. These results demonstrate, across all different mGWAS datasets we used, the potential of using the SRD metric with a cut-off of SRD %8 in order to identify false negatives hits and prioritize follow up studies and experiments. Another interesting case is when the SRD value is large, but the p value is highly significant. In this case, a direct influence of the gene on the associated metabolite is less clear. For example, we noticed a large SRD value for the association between the alkaline phosphate (ALPL) and the phosphocreatine (SRD = 22; p value = 9.
356 3 10 À10 ). One possibility is that this association could have been a false negative. However, digging further into the literature, we found that phosphocreatine can, in fact, be the substrate of the enzyme encoded by the ALPL gene. 44, 45  Thus, the true SRD value for ALPL-phosphocreatine pair should be 0, and the high SRD value computed is a consequence of the current state of the publicly shared knowledge available on KEGG. This result highlights a limitation of our approach, which is the incompleteness of some metabolic pathways in KEGG. Although the SRD metric leverages existing and known KEGG knowledge, our SRD annotation pipeline of mGWAS results can help identify those cases and could be useful to detect gaps and errors in metabolic network databases. Other cases of SRD >8 for highly significant association have been noted in our work, notably in TK study (Figures  2A, 2C,  and 3A ).
Investigation of enzymes or metabolites with an unusual number of infinite or NA values may lead to more complete metabolic pathway databases. Beyond these potential gaps in KEGG, other limitations of this resource are the metabolic reactions provided do not specify cofactors, enzymatic complexes required for the reaction to happen, and directionality, resulting in a decreasing level of complexity and accuracy of the metabolic pathways. Of note, the KEGG overview graph primarily centers on enzymes, with 1,321 genes encoding for enzymes out of the 1,351 genes implicated in pathway reactions (>97%). Enzymes stand out due to their marked substrate specificity, which distinguishes them from transporters and others type of encoding genes. Here, we focused our analysis on associations that mirror the distinctive specificity within the cascade of enzymes and their associated metabolites, which is why we narrowed down our investigation to SNPs mapped to genes encoding for an enzyme.
Future implementations on alternative databases should consider two additional desirable characteristics of KEGG when comparing results: first, KEGG is the only database for which pathway maps were built on known reactions from humans and others species, a clear advantage for non-human mGWAS studies  48, 49  ; second, KEGG pathway maps are built with an explicit labeling of side compounds definition (such as ATP or H 2 O) from KEGG RPAIRS,  50  making their removal from curated reactions possible, which eliminates shortcuts created by their over-representation within the graph when computing SRD. 51  A crucial step for the annotation of gene-metabolite pairs with SRD values is to obtain the KEGG IDs for the genes and metabolites. Although gene names and IDs are highly standardized  52  there is a lack of uniformed nomenclature for metabolites.
In most cases, there are multiple ways to refer to a metabolite, with different studies using different reporting conventions, which complicated the re-analysis of published datasets. For HMC and SCD studies, HMDB IDs 3 were provided by the authors, which could be easily converted to KEGG IDs using the MetaboAnalyst Convert tool. In the case of the TK study, we performed a manual annotation of the common names to KEGG IDs, but this process was time consuming and required having the relevant expertise. This manual work allowed us to include listed metabolites that did not have KEGG IDs, such as acylcarnitines (see Material and STAR Methods). These metabolites are measured in plasma but arise from intracellular metabolism of their acyl-CoA counterparts, which in contrast to acylcarnitines do not cross the plasma membrane.