dataesr/scientific-paragraphs-categorization huggingface dataset:
An important component of data uptake, use and integration is access to the individual platform metadata. Currently animal metadata are not centralized or easily accessed and given that the Joint Technical Commission for Oceanography (JCOMMs) in situ Observing Programmes Support Center (JCOMMOPS) already manages platform metadata for gliders, Argo float and sensors on ships, it follows that as part of the broader and globally comprehensive ocean observing system, the physical environment data from animal-borne tags could also be managed by JCOMMOPS. The basic metadata associated with the observations would ideally include: World Meteorological Organization (WMO) identifying numbers, the principal investigator's name, species name, sensor information from the manufacturer, tag program configurations, sensor calibration, diagnostics and data adjustment coefficients and the quality control details and quality flags.
, 2015). It also has a spatial interface where environmental spatial layers can be added, and data can be discovered by species or location. The Atlas of Living Australia typically ingests data in Simple Darwin Core, a flat file format, which accommodates simple occurrence data well  (Newman et al. , 2018). ZoaTrack records are summarized into this format by representing a track as an occurrence record with a summary footprint over a date range. An implementation like this as a metadata catalog allows tracking data to be discoverable and begins opening possibilities for API development and machine interoperability between other external systems and users. However, there will be challenges in accomplishing the latter as data fields and vocabularies vary among the different systems due to their independent evolution.
Census of Marine Life (http://www. coml. org/) See OBIS below All marine species Global Diversity, distribution, and abundance of all marine Founded in 2000, the Census was funded by philanthropic species foundations and their partners for 10 years to support a global network of researchers, investigating and explaining the diversity, distribution, and abundance of life in the oceans. Legacy projects from the Census are planned to continue into the future. European Tracking Network (ETN; www. sextant. ifremer. fr) Flanders Marine Institute (VLIZ) Cetaceans, fish, pinnipeds, Global Oceanographic and climatological data and animal Brings together European marine researchers that use seabirds, sharks, and turtles movement and behavior; acoustic, archival, and aquatic biotelemetry as a tool. satellite tags Global Seabird Tracking Database (www.
More precisely, the purposes of LAD are: • to determine similarities within the same data set, • to discriminate observations belonging to different data sets. • to deduce logical rules/formulas that explain data sets. As mentioned above, LAD focuses merely on explanation when classification techniques allow the user to build cluster and to assign groups to incoming data. Many applications of logical data analysis have been investigated in medicine  (Reddy et al. , 2008) , industry  (Mortada et al. , 2012; Dupuis et al. , 2012)  or economy  (Hammer et al. , 2012).
Terminology and notation Let us recall the main concepts of logical analysis of data (LAD)  (Hammer et al. , 2004; Hammer and Bonates, 2006; Boros et al. , 2011; Chikalov et al. , 2013). LAD is based on the notion of partially defined Boolean functions. Definition 1. A Boolean function f of n variables, n ∈ N, is a function f : B n → B, where B is the set {0, 1}. Definition 2. A vector x ∈ B n is a positive vector (resp. negative vector) of the Boolean function f if f (x) = 1 (resp f (x) = 0). T ( f ) (resp. F( f )) is the set of positive vectors (resp. negative vectors) of the Boolean function f. In the rest of the paper, Boolean vectors correspond to observations. The set of observations is denoted Ω. Definition 3. A partially defined Boolean function (pdBf) on B n is a pair (P,N) such that P, N ⊆ B n and P ∩ N = / 0.
Such identification are very helpful for building simple and cheap diagnosis routines  (Boureau et al. , 2013). The original files are available 3. Initially, several groups are considered in these instances. Therefore, we have considered the first group of bacteria as the positive group and the union of the other groups as the negative group. Note that similar results have been obtained when considering others groups as positive group. • Instances vote r 4 are also binary data used as benchmarks for classification purpose. Note that these instances have missing data and have been completed randomly. • Instances cr60, os1 and rel1 are datasets corresponding to patients suffering from leukemia. Ob-servations correspond to specific mutated variants of genes that are suspected to play a role in the disease.
Conclusion In this paper we have defined a new algorithm to generate complete sets of prime patterns and strong prime patterns in LAD context. Compared to the state of the art algorithms for these problems, our algorithm is now able to handle larger data sets. The main idea of its resolution process is to use an extension of the LAD (multiple characterization of data) in order to first compute the non dominated solutions and finally obtain all the prime and strong prime patterns. Experiments show the efficiency of our algorithm in term of running times and instance sizes.
An international scientific consortium was put together to analyze the samples, generate data, and develop datasets in coherence with the existing Tara Oceans database. Beyond adapting the extensive Tara Oceans sampling protocols for high-resolution underway sampling, the key novelties compared to Tara Oceans' global assessment of plankton include the measurement of (i) surface plankton and neuston biogeography and functional diversity; (ii) bioactive trace metals distribution at the ocean surface and metal-dependent ecosystem structures; (iii) marine aerosols, including biological entities; (iv) geography, nature and colonization of microplastic; and (v) high-resolution underway assessment of net community production via equilibrator inlet mass spectrometry. We are committed to share the data collected during this expedition, making it an important resource important resource to address a variety of scientific questions.
Model and Satellite Derived Data Mercator Ocean expertise and daily operational service provided a complete set of environmental parameters in the form of oceanographic maps and data from Copernicus Marine Service and Mercator Ocean prediction models including various physical parameters, from the surface to 50 m depth with a spatial resolution of 1/12 •  (Lellouche et al. , 2018) , and chemical and biogeochemical parameters, from the surface to 100 meters deep, with a 1/4 • spatial resolution  (Perruche et al. , 2016). Reanalyses products from those models will be updated at latter stage. Various satellites derived environmental parameters (e. g. , SST, SSS, Chl a, productivity and other) will also be retrieved from NASA and Copernicus Marine Service databases.
Temperature and Salinity The thermosalinograph (TSG, Seabird SBE45) was mounted 5. 5 m downstream from the intake and measured sea surface temperature and conductivity at a sampling frequency of 0. 1 Hz. A second temperature sensor (SBE38) was mounted 1. 5 m downstream from the intake, upstream of the pump and the debubbler. Temperature and salinity measurements were quality checked throughout the expedition. The measurement errors of temperature are on the order of 0. 01 • C, whereas salinity records usually had uncertainties between 0. 01 and 0. 05. Salinity measurements were corrected after the analysis of unfiltered seawater samples and validated against Argo floats data in the vicinity of Tara. A systematic collocation of close Argo profiles in distance/time (±100 km and ±5 days) allowed a point-bypoint comparison.
Instruments were cleaned and filters were changed weekly or before starting a new leg. The datasets were manually checked, periods removed were mainly associated with saturated sensors, accumulation of particles in the BB-box, extended periods without filtered times (failure of switching), and strong changes in flow rate. The quality-checked IOPs can be used to retrieve proxies of phytoplankton biomass (chlorophyll a, with a detection limit of 0. 03 mg m 3 and a maximum relative error of 38%, see  Boss et al. , 2013)  and phytoplankton carbon  (Graff et al. , 2015)  concentrations, particulate organic carbon (with about 20% of variability due partly to the phytoplankton community composition; see  Cetinić et al. , 2012) , relative FDOM (fluorescent dissolved organic matter), with a relative error ranging between 4 and 40% depending on CDOM concentration as a proxy for dissolved organic matter concentrations  (Belzile et al.
Genomic Protocols Genomic analyses from all size fractions are conducted at the Genoscope, CEA, France. The protocol applied for nucleic acid extractions from the size fractions < 0. 22, 0. 22-3 µm, 3-20 µm, 20-2000 µm, and 300-2000 µm is based on simultaneous extraction of DNA and RNA as previously described  (Alberti et al. , 2017). DNA extracts are sequenced to analyze metagenomes and amplicons for eukaryotic (18SV9) and prokaryotic (16SV4V5) metabarcoding. The already analyzed DNA data show that 99% of the extracts are above the 500 ng threshold that allowed library construction with the standardized procedures used in the Tara Oceans project (Supplementary Figure  2 ). Symbiodiniaceae diversity (ITS2)  (LaJeunesse et al. , 2018)  will be analyzed for the 3-20 µm size fraction only according to  Hume et al.
Images generated by the Flowcam and the ZooScan are processed using the same Zooprocess software and the classification of each object is predicted automatically by the Quantitative Imagery Platform and validated with taxonomy experts using the web-based Ecotaxa Application  (Picheral et al. , 2017) , a tool for the taxonomic classification of images which offers the world's largest database of classified planktonic images 2  following the UniEuk taxonomic tree 3.
Data Management Tara Pacific endorses the FAIR principles for scientific data management  (Wilkinson et al. , 2016)  as well as ethical and responsible use of data. With a few exceptions, all physical samples were preserved on board Tara and sent back to partner laboratories for subsequent analysis. Controlled vocabularies describing sampling devices and sample preparation protocols were used throughout the expedition to capture provenance metadata on customized log sheets, and samples were assigned unique identifiers to facilitate their traceability (Table  1 ). Rich environmental context will be generated for each sample, using in situ measurements as well as climatology and remote sensing data obtained from NASA Ocean Color, the European Copernicus Marine Service and Mercator Ocean products.
Legal documents regulating the collection, export and import of samples, as well as links to the CBD's access and benefitsharing clearinghouse 4 will be provided for each sample as a first step toward ethical and responsible use of the Tara Pacific data. The detailed registry of all samples, including their provenance, environmental and legal context, is curated manually using simple semantics that enables machine and human-readable data discovery services. The structure of all data sets and metadata will be consistent and interoperable with that of the Tara Oceans expedition, with improvements when needed. Genomics data will be deposited in free, open access at the European Nucleotides Archive 5 , and environmental data will be deposited in free, open access at PANGAEA 6. The two archives FIGURE 9 | Schematic summary of the systems approach and "end-to-end" plankton sampling adopted during the Tara Pacific expedition.
Microplastic Surface microplastics represent a novel ecological habitat with a clear niche partitioning among bacteria living on plastics, organic particles and surrounding seawater  (Dussud et al. , 2018b). The sampling realized during Tara Pacific will complete the existing databases of surface microplastics spatial distribution and contribute to evaluate the interaction between the microplastics, neuston and plankton from individuals to communities. The geography of plastic fragments is not well understood, and ocean scale datasets like those assembled during the Tara Pacific expedition are important for realistic modeling of the fate of plastic pollution  (Van Sebille et al. , 2015; Onink et al. , 2019). Plastic fragments are rapidly colonized and become a new artificial habitat for pelagic and benthic communities.
CONCLUSION We have entered a period of time where humans have greater impact on the Earth System than any geochemical cycles. The ocean-atmosphere interface represents a critical chokepoint for gas and nutrient exchange between these global engines of geochemistry. These surface boundary layer ecosystems are subject to myriad stressors in an already intense and strained environment. Such added stressors will undoubtedly alter the physico-chemistry of the ocean-atmosphere interface in ways that will impact the ocean's ability to absorb the atmosphere's (elevated) CO 2 levels. These responses will be complex, and their study requires cross-disciplinary and large-scale surveys to begin to map the community changes and possible mechanisms underpinning ocean-atmospheric processes. The underway sampling protocols presented here offer a first step toward these goals, and the coming data will be invaluable for catapulting the ocean-atmosphere science into the modern 'big data' era.
U. Copernicus Marine Service Information and Mercator Ocean products. We acknowledge funding from the Investissement d'avenir project France Génomique (ANR-10-INBS-09). FL is supported by Sorbonne Université, Institut Universitaire de France and the Fondation CA-PCA. The in-line and atmospheric optics dataset was collected and analyzed with support from NASA Ocean Biology and Biogeochemistry program under grants NNX13AE58G and NNX15AC08G to University of Maine. MF, IK, and AV are supported by a research grant from Scott Jordan and Gina Valdez, the De Botton for Marine Science, the Yeda-Sela center for Basic research, and the Sustainability and Energy Research Initiative (SAERI). NCo was supported by a grant from the Simons Foundation/SFARI (544236). NCa and YL were supported by the "Laboratoire d'Excellence" LabexMER (ANR-10-LABX-19) and co-funded by a grant from the French government under the program "Investissements d'Avenir".
Identification of relevant tissues and cell types A total of 7 datasets with tissue/cell type-specific gene expression and chromatin state data were assessed to determine potential enrichment of tissue/cell type-specific categories in MSA heritability. Among all datasets, we identified elevated enrichment for blood/immune and digestive categories in the Roadmap dataset  [22]  (Supplementary Figure  2a  right, and Supplementary Table  2 ). Furthermore, the ImmGen dataset  [23]  highlighted a trend for enrichment for B and Myeloid cells in MSA (Supplementary Figure  2 , b, left and Supplementary Table  2 ). Taken together, these results suggested the relevance of the immune and digestive system in MSA pathogenesis. Though we observed a trend for enrichment for various categories, they were not significant when corrected for multiple testing, most likely due to the small sample size of our MSA cohort (Supplementary Figure  2 , Supplementary Table  2 ).
Furthermore, as it has been shown in various other complex diseases, including neurodegenerative diseases, simultaneous inclusion of common and rare variants increases power to detect the underlying genetic underpinnings for complex diseases  [37] [38] [39] [40]. By leveraging various gene expression datasets, our study showed that MSA heritability is enriched in genes with the highest specific expression in blood and digestive categories in GTEx and Roadmap datasets. Interestingly, we observed evidence of enriched heritability in genes showing elevated expression of immune cells in our study. Though the elevated expression was not statistically significant, this is likely due to the small sample of our MSA GWAS data  [32]. Our FUMA analysis provided further evidence of involvement of a locus on chromosome 5 at 5p13.
Identification of relevant tissues and cell types To understand whether conjFDR genetic-pleiotropy analyses help further to explain the heritability in MSA by identifying genes expressed in certain tissue/cell types, we used a modified LD score regression applied to specifically expressed genes (LD-SEG) method  [36]. In brief, for each gene a t-test was evaluated for the tissue/cell type-specific expression level. Subsequently, the top 10% of genes were selected and a 100 kb window was added around the transcribed region of each selected gene. Finally, stratified LD score regression  [59]  was applied to resulting genetic regions. This procedure was then repeated for each tissue/cell type of interest. We used publicly available tissue/cell type datasets together with corresponding precomputed gene sets and respective LD-scores as described in the LD-SEG method  [36].
A total of seven datasets were used for analyses, including data from GTEx (53 tissues and 13 brain regions)  [60] , Franke lab (152 tissues)  [61, 62] , Roadmap Epigenomics consortium (396 tissues and cell types)  [22] , EN-TEx (93 tissues and cell types), Cahoy et al. [63]  (3 brain cell types), ImmGen Consortium (292 immune cell types)  [23]  and Corces at al. [64]  (13 cell types).
Gene-level association tests The top loci from the conjFDR analysis were further evaluated to determine the role of common and rare variants in IBD/CD and MSA cohorts respectively. An exome-wide chip covering a total of 205,313 SNPs was used to determine the role of common and rare variants in IBD/CD. RAREMETALWORKER was used with the first ten principal components from PCA as covariates to analyze individual CD and IBD studies and to generate association summary statistics  [68]. The exome chip genotype data was converted into Variant Call Format (https://github. com/samtools/hts-specs) format with PLINK  [56] , and variants were functionally annotated with EPACTS (v3. 2. 3; http://genome. sph. umich. edu/wiki/EPACTS). A two-step approach was followed: (i) SKAT test  [30]  was performed in German CD cohort (exome array study including 4,989 cases and 16,307 controls, using the chip with 205,313 mostly rare coding variants) followed by replication (ii) in a combined IBD cohort (15,236 cases and 34,668 controls).
The total number of genes covered in CD cohort was: n = 15,975 for all variants, n = 15,742 for rare variants, and in IBD cohort was: n = 16,306 for all variants, n = 16,138 for rare variants. SKAT test in both cohorts was conducted using all coding variants within identified genes (Table  3 ). P-values resulting from these tests were corrected for multiple testing using Bonferroni correction. Whole exome sequencing data from 358 European-ancestry MSA patients and 1,297 neurologically healthy, European controls were generated using the SureSelect Exome target enrichment technology according to the manufacturer's protocol (Agilent, CA, USA). Sequence alignment to the human reference genome (hg19) and variant calling were performed using the Genome Analysis Toolkit. After removal of duplicate samples using Picard software, stringent quality control filters were applied, including the removal of non-European ancestry individuals and cryptically related individuals, exclusion of individuals with the discrepancy between reported sex and genotypic sex and individuals with high genotype missingness or extreme heterozygosity.
Samples were homogenized in TRIzol reagent (Life technologies) with ULTRA-TURRAX T-8 basic tissueruptor (IKA) and RNA was isolated following the manufacturer´s instructions. RNA samples were retrotranscribed into cDNA by using the High-Capacity cDNA Reverse Transcription Kit (Applied-Biosystems). Analyses were performed in a CFX96 Touch™ Real-Time PCR Detection System (Bio-Rad) using iTaq universal probes supermix (Bio-Rad). Gapdh mRNA levels were used as an internal normalization control. All statistical analyses were conducted using the software Graph-Pad Prism 7 (Graphpad Software). The mean ± S. E. M was used to present the results. Comparisons were performed with multiple t-tests with Bonferroni-Dunn correction. An adjusted pvalue <0. 05 was considered statistically significant. Scatter plot of the -log10(conjFDR) plotted against -log10(PeQTL) (left), regional association plot for -log10(conjFDR) values (top right), regional association plot for -log10(PeQTL) (bottom right).
ACKNOWLEDGMENTS This work is supported by the grants from  Multiple System Atrophy Coalition, USA  (to M. S. ). This work was further supported by the grants from the  German Research Council  ( DFG/SH 599/6-1  to M. S. ), the  EU   Joint Program-Neurodegenerative diseases (JPND ;  COURAGE-PD  to M. S. and T. G. FKZ  01ED1604 ), and  Michael J Fox Foundation  (to M. S. ). This work was supported by the  Research Council of Norway  (# 223273 , # 225989 , # 248778 ) South-East Norway Health Authority (# 2016-064 , # 2017-004 ), KG  Jebsen Stiftelsen  (# SKGJ-Med-008 ). (to A. A. S. , O. A. A, O. F. , K. S. O. , S. B. )This work was supported in part by the  Intramural Research Programs of the National Institute of Neurological Disorders and Stroke (NINDS) , and the  National Institute on Aging (NIA) : project numbers  Z01-AG000949 ,  1ZIA NS003154.
This work was supported by a grant of the  Austrian Science Fund (FWF)   F4414  (to NS). This work was supported by the  German Federal Ministry of Education and Research (BMBF)  within the framework of the e:Med research and funding concept (SysInflame grant  01ZX1606A ;  GB-XMAP grant 01ZX1709 ). The project was funded by the  Deutsche Forschungsgemeinschaft (DFG, German Research Foundation ) under Germany`s Excellence Strategy -EXC  2167-390884018. The project received infrastructure support from the  DFG Excellence Cluster No. 306  " Inflammation at Interfaces " and the  PopGen Biobank (Kiel, Germany ). The  KORA research platform (KORA, Cooperative Research in the Region of Augsburg)  was initiated and financed by the  Helmholtz Zentrum München -German Research Center for Environmental Health , which is funded by  BMBF  and by the  State of Bavaria.
We investigated whether the geographical region influences the observed RCS distributions. We used passerine-type echoes only because they are classified with high credibility, are abundant at each site and cover the entire range of WBF. We tested the dependency of the 0. 9-quantile of the RCS (square-root transformed; using the 0. 95-quantiles lead to similar results) on 2-Hz WBF interval (as ordered factors), adding the site identity as a random intercept in linear mixed-effects models as implemented in the 'lme4' R-package  (Bates et al. 2015, R ver. 3. 4. 3), assuming a Gaussian distribution of the residuals.
Discussion Inexperienced researchers using radar systems to quantify bird movements often assume that the coverage of a radar is related to the distance at which some birds can still be detected. This study provides a framework for the accurate quantification of avian movements with radar, taking into account the size-specific monitored volume. Using data collected from distinct areas across Europe, and a broad variety of flying animals, we estimated the size of a bird based on the WBF, independent of the echo power.