dataesr/scientific-paragraphs-categorization huggingface dataset:
3 Third, the information required to ensure appropriate use of such data by secondary users requires delineation  (42). These challenges are not unique to AIRR-seq data. Indeed, the need for shared standards has been recognized and addressed for previous high-throughput technologies  (43) , including microarray data  (44). Another significant challenge for AIRR-seq data is that the processing pipeline between the experiment and the ultimate analysis of the data is lengthy and specialized  (45) (46) (47) (48) (49) (50) (51) (52) (53) (54) (55) (56) (57) (58) (59). Beyond the steps required to process any HTS data, the annotation required of AIRR-seq data is unique to these genes and subject to substantial uncertainty  (52). Unlike other genes, the antigen receptors of adaptive immunity are assembled through the recombination of randomly chosen gene segments, with nontemplated nucleotides added to the junctions and nucleotides nibbled away from the gene segments  (60).
A BrieF HistOrY OF tHe Airr cOMMUNitY The AIRR Community was established in 2015 at a meeting organized by Felix Breden, Jamie Scott, and Thomas Kepler in Vancouver, BC, USA to address these data sharing challenges. Membership in the AIRR Community is open and is intended to cover all aspects of AIRR-seq technology and its uses. Membership includes researchers expert in the generation of AIRR data; statisticians and bioinformaticians versed in their analysis; informaticians and data security experts experienced in their management; basic scientists and physicians who turn to such data for critical insights; and experts in the ethical, legal, and policy implications of sharing AIRR data. In 2015, the AIRR Community formed three Working Groups. The Minimal Standards Working Group was tasked with the development of a set of metadata standards for the publication and sharing of AIRR-seq datasets.
The Tools and Resources Working Group is focused on the development of standardized resources to facilitate the comparison of AIRR-seq datasets and analysis tools, including collection, validation, and nomenclature of germline alleles. Finally, the Common Repository Working Group is working to establish requirements for repositories that will store AIRR data. The Working Groups are dynamic and often collaborate with each other, as methods evolve and applications of standards in one area (for example, metadata standards) impact other areas (data repository requirements). Full recommendations and membership lists for the Working Groups as well as video recordings of the 2016 AIRR Community meeting are available at http://www. airr-community. org. At the June 2016 meeting held at the National Institutes of Health (NIH), the AIRR Community ratified an initial set of recommendations that are summarized herein.
DAtA GeNerAtiON Due to the complexity and diversity of the data sets being generated, the AIRR Community is developing best practices for the generation of AIRR-seq data. Such best practice guidelines will include, at a minimum: standard operating procedures for cell isolation and purification, including panels and gating strategies for flow cytometry; primers and protocols for amplification and sequencing of BCR or TCR rearrangements; and a clear description of library preparation and sequencing. Nomenclature is particularly important when it comes to the multiple stages of sample processing and data analysis. For example, what is meant by "raw data" differs among investigators, compounded by the fact that there are multiple levels of data preprocessing. At present, the AIRR Community recommends that: (1) experimental protocols should be made available through a public repository granting digital object identifiers; (2) the change history of the experimental protocols, including details of what was changed and when the changes were made, should be made publicly available through the same repository; and (3) biological materials (e.
DAtA sHAriNG For transparency and reliable reuse, experiments need to be sufficiently well annotated to allow evaluation of the quality of individual datasets and comparability of different datasets. Therefore, the AIRR Community has developed experimental metadata standards for AIRR-seq data generation, processing, and quality control. The data consist of the raw sequences and the processed sequences, while metadata include clinical and demographic data on study subjects and protocols for cell phenotyping, nucleic acid purification, AIRR amplicon production, HTS library preparation and sequencing, as well as documentation of the computational pipelines used to process the data. In publications or other forms of data sharing, these metadata sets and their components should be described in sufficient detail such that a person skilled in the art of AIRR sequencing and data analysis will be able to reproduce the experiment and data analyses that were performed.
A manuscript describing a complete model for AIRR-seq data and metadata, and standardized terminology will soon be submitted. Data sharing is also premised on the user's ability to locate and access the data. The AIRR Community recommends that all published AIRR-seq data be deposited in designated public repositories that adhere to the AIRR Community minimal standards guidelines, namely, that the data should be made available under the least restrictive terms possible. Limited exceptions to respect commercial interests in intellectual property rights are under consideration by the AIRR Community. To facilitate data sharing, the AIRR Community is also establishing an AIRR Data Commons, comprised of multiple, distributed repositories optimized for storing and querying AIRR-seq data, and supported by a centralized Gateway.
For studies using AIRR-seq data from human subjects, data must be collected following a protocol that has been approved by the researcher's Institutional Review Board, which oversees human subjects' protections and ensures that all studies are performed in a legal and ethical manner  (63). Human subjects must provide informed consent, and there should be broad agreement in the consent language regarding the confidentiality of medical information and the use of AIRR-seq data and metadata for future research. Without such provisions, the data in the database may be too constrained, with respect to time or breadth of investigation, to be usable by investigators other than the initial data generators. Whenever data or other items of potential commercial value are shared with others, the individuals who generated and deposited the data should to be given proper credit.
Hence, users of the database should, at a minimum, credit the data depositors in any publication or grant application. One mechanism whereby these rules could be followed is to create an online form that must be completed before access to the database is granted. Such a form would essentially be a contract for using the data. Enforcement of the terms of the contract could include monitoring of data use and denying access to the database should the terms of the datause agreement be violated. To facilitate broad access to and use of AIRR-seq data, the data should be made available under the least-restrictive terms possible. The default data sharing policy should be to deposit data in a public domain database with no restrictions over deposit, access, storage, curation, or use. For data deposited in public domain databases/repositories, neither the depositors nor the repositories should be permitted to interfere with access to and use of the data by others, including through the assertion of any intellectual property rights.
DAtA ANALYsis The AIRR Community strongly advocates the use of statistical methods for data analysis and hypothesis testing. Statistical methods systematically characterize error, quantify uncertainty, and provide a measure of confidence for inferences. Statistical methods also form the basis for data analysis in all other realms of biomedical and scientific research and should be adopted for AIRR-seq data. Expanded production of AIRR-seq data has been supported by a proliferation of computational tools for their processing and analysis, including tools for variable region gene annotation, inference of clonal history and partitioning and visualization  (16, 46-52, 55, 56, 64-69). To encourage broad and well-informed use of these tools, the AIRR Community recommends that analysis software be released under an Open Source Initiative approved license, hosted on a publicly available website or repository with versioning, and be designed for modularity and inter-operability with other software.
The AIRR Community will promote best practices in AIRR-seq data analysis by: (1) developing and publishing common criteria for the evaluation of statistical methods; (2) providing common "gold-standard" datasets of multiple types for use in software development, testing, and calibration; and (3) establishing best practices for data sharing and analysis software platforms. cONcLUsiON Members of the AIRR Community have worked together for over 2 years with enthusiasm, driven by the belief that optimizing the reproducibility and sharing of AIRR-seq data will have a profound and positive effect on biomedical research and patient care. To encourage widespread adoption, the AIRR Community recommends that journals and funding agencies require AIRR-seq data be made available through a public data repository after publication or as negotiated in data-sharing agreements for unpublished data.
The success of this initiative is also critically dependent upon acceptance by the researchers who generate and use AIRR-seq data. While members of the AIRR Community have tried to be inclusive through developing contacts with researchers in the field and extensively advertising the annual meetings, there are likely to be many researchers who generate, analyze, and use AIRRseq data, who are not aware of the AIRR Community initiative. Community "buy in" results from creating data standards that are transparently developed through public discussion, robustly evaluated, and periodically updated as the field advances. This Perspective represents an open invitation to the larger scientific community to participate in and adopt the AIRR initiative. To that end, we welcome feedback on this Perspective and on the AIRR Community's efforts to date.
AcKNOWLeDGMeNts Many of the ideas presented herein evolved over the course of AIRR Community meetings and Working Group meetings. The AIRR Community initiative and Community meetings were supported by CIHR, NIH (Jon Warren and Joe Breen), NIH R13-AI116349, P01-AI106697, R01-AI097403 and P30-CA016520, GenMab, The Antibody Society, CHAVI, the IRMACS Centre, Simon Fraser University, Illumina, Genentech, TTP Labtech, Grifols, and Amgen.
Environmental conditions affecting both avian reservoir hosts and the mosquito vector populations, as well as human behavior and habitat modifications, may regulate WNV amplification and subsequently trigger an outbreak  (Gibbs et al. , 2006). The environmental conditions associated with the onset of WNV human cases in Europe, despite being investigated by multiple studies, are partly unknown  (Paz and Semenza, 2013 , Tran et al. , 2014 , Marcantonio et al. , 2015 , Semenza et al. , 2016). The deficiency of complete and homogeneous continental-wide datasets is one of the underlying reasons for this lack of knowledge. On the contrary, datasets collected at a more local scale, even though representative of only a fraction of the European continent, may provide more robust results. These results may, in certain instance, be transferred to the continental scale.
Given that temperature is one of the main drivers of ecological processes related to vector-borne viruses carried by mosquitoes and that we have the data and tools available to get temporally and spatially continuous temperature time series, our question is then: Can MODIS LST (and derived LST indices) inform us about spatial and temporal trends or changes in temperature that might have contributed to the occurrence of WNV outbreaks in Greece? Here we present a first approach to the problem, however our ultimate goal is to study and model the relationship of different environmental variables and the incidence of human WNV infections in Greece. In this first paper, we present a yearly analysis of LST and derived related variables in sites with reported cases, with the intention of identifying temperature related differences in the periods before and after the first outbreak and municipalities with and without reported cases.
MAIN BODY 2. 1 Materials and Methods 2. 1. 1 Data used: Our study area is Greece which has a surface of 131. 957 km 2. For the reconstruction of satellite data however, we used a larger area which boundaries are: North: 42°1'12"N, East: 15°24'6"E, South: 34°41'47"N, West: 34°23'53"E. We used the daily LST Day and Night from MOD11A1 and MYD11A1 version 6 products (1 km of spatial resolution) from January 1 st , 2003 to December 31 st , 2016. The emissivity band in the MODIS LST products and the 30 arc-seconds Global Multi-resolution Terrain Elevation Data 2010 (GMTED2010) were used as covariates. Greek meteorological stations from the public database Global Summary of the Month (GSOM 1  ) were used as validation set for the obtained reconstructed LST. The epidemiological dataset was obtained by the European Centre for Disease Prevention and Control (ECDC).
LST data processing: The raw data downloaded from MODIS LP DAAC site (4 images per day) were mosaicked and reprojected to Lambert azimuthal equal area (EU LAEA, EPSG code 3035) with nearest neighbor as the resampling method. Nearest neighbor sampling was chosen in order to preserve the bit pattern in the quality assessment (QA) band. The reprojected MODIS LST values were then filtered with the mandatory and LST QA band allowing for an LST error lower than 3 K. Our LST reconstruction method consisted of temporal interpolation to partially reconstruct Day and Night daily LST (fill short gaps in time) followed by spatial interpolation with covariates and outlier filtering to fill remaining gaps in space. This was done for each time series separately (MOD11A1 Day and Night, MYD11A1 Day and Night).
Data analysis: We aggregated the daily reconstructed LST time series into months, seasons and years by means of arithmetic average. This yielded the average temperature, the average minimum temperature and the average maximum temperature for each period (i. e. : month, season, year). Seasons were defined as follows: winter (December, January, February), spring (March, April, May), summer (June, July, August) and autumn (September, October, November). We then estimated a series of biologically meaningful variables that are known or hypothesized to affect mosquito populations, WNV transmission or disease risk. The thresholds used for deriving LST-related variables were drawn from bibliography on both mosquitoes survival and development and virus amplification  (Rueda et al. , 1990 , Vinogradova, 2000 , Reisen et al.
2. 1. 4 Software: MODIS data was dowloaded by means of pyModis library 2. All the remote sensing processing and analysis was done in GRASS GIS  (GRASS Development Team, 2017). For plots and comparisons, GRASS vector maps were read into R (R Core Team, 2017) through the rgrass7 and sp packages  (Bivand, 2016 , Bivand et al. , 2013). Further plots were done with ggplot2 and spacetime packages  (Wickham, 2016 , Bivand et al. , 2013).
Sensors such as MODIS on board of Terra and Aqua satellites provide temporally and spatially continuous data. However, remotely sensed LST is not free of problems, the main issue is the occurrence of gaps in the data due to cloud cover. Hence, LST needs to be gap-filled first to become fully useful  (Neteler, 2010 , Metz et al. , 2014). A general evaluation of our reconstruction showed good global relationship between air temperature from meteorological stations in Greece and reconstructed MODIS LST. Since it was previously suggested that climatic conditions (higher temperatures, especially) might have favored the occurrence of the 2010 WNV outbreak in Greece  (Danis et al. , 2011)  and Europe in general  (Paz and Semenza, 2013) , our main objective was to identify temperature-related variables that might show temporal differences in localities with reported human cases and spatio-temporal differences in areas with and without WNV infection.
CONCLUSIONS This study highlights the usefulness of remote sensing, and especially LST, for the characterization and quantification of temperature changes. The major advantage of MODIS LST is that it captures climatic conditions with short revisit time and global coverage. This might reduce the need of meteorological field data, especially when resources are limited. Satellite tracking of LST is indeed useful and might be complementary to mosquito and virus surveillance and monitoring activities. Furthermore, the set of LST indices derived might be extended to other areas and vector-borne pathogens whose ecology and biology is strongly connected to temperature variations. Besides the application and the results per se, which are yet preliminar, the relevance of this study is also related to the use of open data and free and open source software that provide the advantage of automatization and reproducibility.
| Data selection The power of resolution for the individual identification was assessed by calculating the probability of identity P(ID) and the probability of identity between siblings P(ID)sib using GenAlEx 6. 501  (Peakall & Smouse, 2012). These probabilities also allowed us to determine the minimum number of amplified loci required to have no ambiguity for individual identification. According to  Waits, Luikart, and Taberlet (2001) , we used 0. 01 as threshold for the P(ID). We removed from our dataset any individual typed under the minimum number of loci determined. Species identification for each individual was determined using the Bayesian software STRUCTURE 2. 3. 4  (Falush, Stephens, & Pritchard, 2003; Pritchard, Stephens, & Donnelly, 2000). We used an admixture model with correlated allele frequencies between populations and the option POPINFO = 0.
The program was run with a Monte Carlo Markov chain length of 1. 000,000 after a burn-in of 100,000 iterations, and from one to four genetic clusters (K = 1-4). Ten independent runs were carried out for each value of K, and the information from the outputs of the 10 runs for each K was compiled using CLUMPP  (Jakobsson & Rosenberg, 2007). We determined the number of clusters (K) that best describe the data following the method of  Evanno, Regnaut, and Goudet (2005)  implemented in STRUCTURE HARVESTER online web 0. 6. 94 (Earl & VonHoldt, 2011). We then chose to discard any individual for which the genetic assignment did not match the morphologic identification (individuals had to show a probability of assignment higher than 0. 9 to the opposite cluster to which they had been morphologically assigned).
Once the species of each individual was determined, null alleles for each locus in each species were looked for with the software Micro-Checker 2. 2. 3  (Van Oosterhout, Hutchinson, Wills, & Shipley, 2003). The significance of null allele frequencies was then assessed using a binomial exact test following de Meeüs, Béati, Delaye, Aeschlimann, and Renaud  (2002). Dropout probability per locus was assessed according to De Meeûs, Humair, Grunau, Delaye, and Renaud (2004). All subsequent analyses were performed with and without the loci for which we detected significant signs of null allele frequencies or dropout. Finally, we tested for deviation from Hardy-Weinberg equilibrium and linkage disequilibrium for each pair of loci separately for brown hares and mountain hares with FSTAT 2. 9. 3. 2  (Goudet, 1995).
| Detection and characterization of hybrids In each cluster, we selected the eighty individuals best assigned. All selected individuals reached a posterior probability larger than 0. 99. They were then used to simulate parental and hybrid (F1, F2, T A B L E 1 Description of the microsatellite markers of the 12-plex. The accession number in NCBI is given for each locus if the information is available. The pigtails are reported in bold in the reverse sequence of the primers. The given concentration corresponds to the final concentration of each primer in the primer solution.
Amorces 5′-3′ Fragment length Backcrosses) genotypes with the function hybridize implemented in the R package adegenet 2. 0. 1  (Jombart, 2008). A total of 1,800 simulated genotypes were generated and analyzed using NewHybrids 1. 1  (Anderson & Thompson, 2002)  in order to estimate the resolution power of the 12-plex for the detection of hybridization. We considered that an individual was assigned to a genotypic class when the probability for this class exceeded 0. 90  (Godinho et al. , 2011).
These individuals as well as all individuals genotyped with less than nine markers were removed from the analysis. The final dataset, after PI calculations and STRUCTURE analysis, resulted in 521 brown hares and 122 mountain hares. Deviation from Hardy-Weinberg equilibrium varied among loci within both species. Some loci that were out of HW equilibrium: SAT12 and LSA8 in the mountain hare, SOL33 in the brown hare, LSA3, SAT13, and SOL30 in both species. There was no linkage disequilibrium between any pair of loci after the Bonferroni correction. Only the locus LSA3 showed significant signs of null alleles. We also found signs of allelic dropout for the loci LSA6 and SAT12. In following analyses, we compared the results using 12 markers and nine markers (LSA3, LSA6, and SAT12 removed). The same 11 errors of species assignation also appeared with the set of nine markers.
g. , based on feces), often associated with a higher rate of amplification failure. The analysis of a complementary dataset (unpublished data) made of noninvasive samples (feces) of mountain hares (n = 38) showed a microsatellite amplification success reaching 97. 71%, quite similar to the amplification success obtained in  Rehnus and Bollmann (2016)  on noninvasive samples (feces). The estimates of allelic dropout (0. 17 across loci) and false allele (0. 014) were consistent with the ranges obtained in previous noninvasive genetic studies  (Frantz et al. , 2003; Steyer, Simon, Kraus, Haase, & Nowak, 2012). In particular, this panel may be used to estimate other population parameters such as population size and survival rates when more common tools (such as direct counts or telemetry for example) reveal impractical  (Kohn et al.
ACKNOWLEDGMENTS The molecular analyses were funded by the  National Hunting and Wildlife Agency (ONCFS) : funding contracts  2004/08 ,  2005/19/6171  and  2009/22/6171  and the  CNRS. We are very grateful to hunters and to technical staff of ONCFS and of Departmental Hunting Federations of south-eastern France (SD and  FDC of Ain, Alpes-de-Haute-Provence, Hautes-Alpes, Alpes-Maritimes, Ardèche, Drôme, Isère, Loire, Haute-Loire, Lozère, Puy-de-Dôme, Rhône, Savoie et Haute-Savoie ) who collected genetic samples for this study. Particularly, we wish to thank  P. Auliac ,  C. Blachier ,  R. Corti ,  G. Coursat ,  P. Crégu ,  B. Degrange ,  P. Douvre ,  F. Étienne ,  P. -F. Galvin ,  J. Hureau ,  N. Jean ,  F. Normand ,  C. Randon ,  C. Rieutort , P. Roche,  P. Sibut ,  D. Siméon ,  P. Vika ,  F.
He concluded that external factors (wind, topography, vegetation) probably had a significant impact on the efficiency of MM snow production. The present study aims to provide a detailed description of the seasonal evolution of a ski slope snowpack in operational conditions with a high spatial resolution (0. 5 m grid), including the additional MM snow from snowmaking methods. Equivalent water masses of MM snow piles were measured prior to any action by the grooming machines, and both snow depth (SD) and snow water equivalent (SWE) of the prepared ski slope were observed on several occasions. These observations were crossed with all available data on snow production (water flow, temperature, wind) and with the results of simulations using a detailed physically based snowpack model  (Spandre et al.