dataesr/scientific-paragraphs-categorization huggingface dataset:
Virtual Machines can be used to package up all the original settings and dependency libraries that are need to re-enact a workflow. Similarly packaging tools such as Docker  [21]  or ReproZip  [17] , can also help users to create relatively lightweight packages that include all the dependencies required to reproduce a workflow or a computational experiment. We too have a zip-based serialisation of our Workflow Research Objects described in the RO Bundle Specification  [43]. These approaches differ with respect to ours in that they lack a structured description of the aggregation. As a result they lack a convenient mechanism to attach arbitrary annotations. They are also limited to aggregating resources that can be directly serialised, and lack the ability to describe an aggregation that includes remote resources, such as large third party databases.
Currently it is deployed and used in data management infrastructure developed for the large scale scientific facilities, such as the ISIS Neutron Source [52] and the Diamond Light Source  [53]. The model provides a hierarchical way to manage scientific investigations, by its research programme, projects and studies, and a way to categorise datasets into collections and files and associate them with individual investigations. Compared with Workflow Research Objects, CSMM does not provide constructs for specifying workflows or capturing their provenance traces.
Unlike Workflow Research Objects, SPP does not cater for the description of workflows, their provenance traces, or their evolution. ReproZip  [55]  is a tool that record workflows of command-line executions and associated resources, including files, dependency libraries, and variables. It then create package that can be used to rerun and verify the reproducibility of such workflows. Compared with Workflow Research Objects, Reprozip is confined to capturing command-line executions that invoke local programs. In Workflow Research Objects, we are targeting workflows that make use of distributed services that are not necessarily accessible locally. Moreover, Reprozip does not capture information about the evolution of workflows over time. They adopt proprietary language for workflow specifications.
Provenance-To-Use (PTU)  [18]  is similar to ReproZip. PTU relies on a user-space tracking mechanism for better portability instead of using a kernel-based provenance tracing mechanism. Similar to ReproZip, PTU adopts proprietary language for specification, and do not capture evolution of the workflow specification. Science Object Linking and Embedding (SOLE) is a system that allows linking articles with science objects  [56]. A science object can be a source code of a software, a dataset or a workflow. SOLE allows the reader (curator) to specify human-readable tags that links the paper with science objects. It transforms each tag into a URI and points to a representation of the corresponding science object. While their objective is similar to ours, the authors of SOLE take the view that the scientific article is the main object that contains links to other (science) objects.
While this approach is desirable, it is not always possible. In many cases, scientists want to use datasets and tools that are remote and cannot be locally deployed, either because the providers of those datasets and tools do not wish to provide access to their resources or because they are large or computationally expensive and cannot be deployed locally. Our approach allows scientists to describe the use of remote resources to perform their analysis, and the means to gather information about those resources and their relationships. While this approach does not guarantee full reproducibility, we think that it is more realistic, and is a step towards enabling reproducibility.
g. the EU Scape  24  project or Timbus 25  project) or workflow-based scientific research (e. g. , the EU BioVel 26  project). In our ongoing work, we seek to collaborate with these communities, as well as others, such as Open Access publishers (e. g. , GigaScience  27  ) and Digital Libraries (e. g. FigShare  28  or Dataverse  [63] ) to improve the Workflow Research Object concept and vocabularies. We also intend to align our ontologies with existing similar standards and initiatives, such as ISA, OPMW and D-PROV. We believe that the work presented in this paper has the potential to: -Facilitate the process by which they package and annotate the resources necessary for preserving their scientific workflows. -Encourage them to reuse existing workflows. For example, users will have elements that allow them to understand the workflow that they will be reusing, e.
Acknowledgements We warmly thank  Stian Soiland-Reyes  from the  University of Manchester  who was instrumental in the development of the ontologies, and  Pinar Alper  from the same university who gave us useful feedback on the wfdesc and the wfprov ontologies. We would also like to thank the members of the Wf4ever and myGrid projects, and the anonymous reviewers for their comments. The research reported in this paper was supported by the  Wf4ever  project ( EU FP7 STREP 270192 ) and the  myGrid platform for e-Biology  (award  EP/G026238/1 ).
wf4ever-project. org/rodl/ROs/data_interpretation-2-snapshot/ inputs/gene_IDs. txt</uri> annotate_genes_biological_processes. t2flow</uri> </binding> </binding> </result> </result> <result> </results> <binding name="inputValue"> <uri>http://sandbox. wf4ever-project. org/rodl/ROs/mypack-2-annotate_gene_list. bundle/inputs/ </sparql> predefined_conceptset_id. txt</uri> </binding> </result> <result> <binding name="inputValue"> <uri>http://sandbox. wf4ever-project. org/rodl/ROs/mypack-2-annotate_gene_list. bundle/inputs/ database_name. txt</uri> </binding> </result> <result> <binding name="inputValue"> <uri>http://sandbox. wf4ever-project. org/rodl/ROs/data_interpretation-2- annotate_genes_biological_processes_xpath_cpids_only_cpids-run. bundle/inputs/ predefined_conceptset_id. txt</uri> </binding> </result> <result> <binding name="inputValue"> <uri>http://sandbox.
wf4ever-project. org/rodl/ROs/data_interpretation-2- annotate_genes_biological_processes_xpath_cpids_only_cpids-run. bundle/inputs/ database_name. txt</uri> </binding> </result> <result> <binding name="inputValue"> <uri>http://sandbox. wf4ever-project. org/rodl/ROs/data_interpretation-2- annotate_genes_biological_processes_xpath_cpids_only_cpids-run. bundle/inputs/cutoff. txt</uri> </binding> <http://ns. taverna. org. uk/2010/workflowBundle/f7b17c46-a5c5-428f-ad45-a0b0f4e85935/workflow/Annotate_gene_list_w/ </result> processor/Merge_String_List_to_a_String_5/> <result> a wro:Resource. <binding name="inputValue"> <uri>http://sandbox. wf4ever-project. org/rodl/ROs/data_interpretation-2-<http://ns. taverna. org. uk/2010/workflowBundle/f7b17c46-a5c5-428f-ad45-a0b0f4e85935/workflow/Annotate_gene_list_w/ annotate_genes_biological_processes_xpath_cpids_only_cpids-run.
Section 4 is dedicated to the theoretical properties of our estimator. The numerical results are presented in Section 5. They have been obtained thanks to the R package HiLMM that we have developed and which is available from the Comprehensive R Archive Network (CRAN). In Section 6, we provide some additional comments on our work as well as some prospects such as the estimation of the proportion q of non null components in the random effects. Finally, the proofs are given in Section 7.
Hence, several approximations have been proposed such as the AI algorithm  (Gilmour, Thompson and Cullis (1995) ) which is used for instance in the software GCTA (Genome-wide Complex Trait Analysis) described in  Yang et al. (2011). Other approximations have also been proposed in the EMMA algorithm  (Kang et al. (2008) ). For further details on the different approximations that could be used we refer the reader to  Pirinen, Donnelly and Spencer (2013). The latter paper proposes another methodology for estimating the heritability which consists in rewriting Model (1) with the parameters (6) and in using an eigenvalue decomposition of the matrix R. Further details on their methodology are given hereafter. According to the numerical experiments conducted in Pirinen, Donnelly and Spencer (  2013 ) their approach has the lowest computational burden among the available algorithms.
d. f of a standard Gaussian random variable for η = 0. 5 and different values of a. We can see that the Gaussian p. d. f fits well the data in all the considered cases. We also display in Figure  4  the values of n -1/2 2γ -2 n and the empirical standard deviation of (ηη ) averaged over all the experiments. As shown in Theorem 3, we also observe empirically that both quantities are very close. In practice, the value of γ -1 n (n/2) -1/2 can be used for deriving confidence intervals for η. As we can see from Figure  4 , our approach leads to very accurate confidence intervals for a larger than 0. 1 even in finite sample size cases. Let us now compare our results with those obtained with the software GCTA. As we can see from Figure  5  which displays the boxplots of η for different values of a when η = 0.
Morphometric analyses For each male, landmarks were manually positioned on the pictures of the ventral branches (Figure  1 , B9-F9) using the software tpsDIG2 version 2. 1  (Rohlf 2006 ) and compiled in a. tps file as a set of five x,y coordinates (forming one configuration). With the full set of configurations (Supporting Information, File S1), we performed a full generalized Procrustes analysis (GPA) using the function "procGPA" of the R package "shapes" (version 1. 1-9) written by I. L. Dryden and available on the CRAN (http://cran. r-project. org). This function performs an optimized superimposition of the configurations using translation, rotation, and scaling and then a principal component analysis on the Procrustes tangent space coordinates  (Dryden and Mardia 1998). The generalized Procrustes analysis was carried out on the backcross progeny dataset only (n = 365) or on the full dataset (n = 507) comprising parents (n = 48 D.
This dual indexing protocol can provide extremely high levels of multiplexing. Sometimes, WMD-MSG leads to unacceptably high dropout of samples when very large numbers of samples (. 500) are processed in a single library. Future optimization of the WMD-MSG protocol may allow improved normalization of read counts among samples, although the current performance of the protocol was sufficient for our study of 384 individuals. A detailed protocol for WMD-MSG is provided as Supplementary Material. The WMD-MSG library was sequenced on an Illumina HiSeq 2500 and the fastq sequence file was analyzed directly with the MSG software, which has been updated to parse fastq files that contain WMD-MSG barcodes (github. com/JaneliaSciComp/msg). In total, 189,819,468 reads were collected for 384 individuals, among which 365 had reliable phenotypic data.
The two species parental genomes used in MSG were prepared by reducing the level of intraspecific polymorphism by applying the script DisambiguateGenomes. py (github. com/dstern/Disambiguate-Genomes) sequentially to genomes updated with whole genome sequencing Illumina reads from three strains of D. yakuba and three strains of D. santomea. The D. yakuba reference genome that we updated is dyak-all-chromome-r1. 3 available from FTP at Flybase (strain Tai18E2 The resultant posterior probabilities of ancestry ("soft genotypes") were thinned to informative markers using the script pull_thin_tsv. py (https://github. com/dstern/pull_thin) (File S8, File S9). Markers were considered informative when the conditional probability of being homozygous differed by more than 0. 05 from their neighboring markers.
QTL mapping QTL mapping was performed using R/qtl  (Broman et al. 2003; Broman and Sen 2009)  (File S10) package version 1. 32-10. Briefly, the principle of QTL mapping is to correlate segregating genetic markers with trait values to identify chromosome regions that significantly affect the phenotype of interest, here the shape of the ventral branches, when substituting a D. santomea allele for a D. yakuba allele. Posterior genotype probabilities, estimated directly by the Hidden Markov Model implemented in MSG, were imported into R-qtl as a "cross" object using the R function read. cross. msg (https://github. com/dstern/read_-cross_msg). In our D. santomea backcross population we did not find significant departure from the expected distribution of 50% of D. santomea alleles and 50% of D. yakuba alleles at each marker position.
For all traits (BC-PC1, All-PC1, centroid size, spine thrust, see Results and Discussion for details), we performed genome scans with a single QTL model ("scanone") using the Haley-Knott regression method  (Haley and Knott 1992) , which performs well with datasets having high genotype completion  (Broman and Sen 2009). We determined genomewide statistical significance using 10,000 permutations  (Churchill and Doerge 1994). Two QTL peaks above the 1% significance threshold were detected. Both QTL were incorporated into a multiple QTL model using the "fitql" function and we tested for possible interactions between loci. To refine the position of the QTL in the multiple-QTL model, we used the function "refineqtl", which uses an iterative algorithm to scan neighboring sites for higher LOD (logarithm of odds) score values, taking into account the already found loci  (Broman and Sen 2009).
We analyzed individual landmark configurations from D. santomea parents, D. yakuba parents, F1 hybrids, and backcross progeny altogether by performing a Procrustes superimposition  (Gower 1975; Rohlf and Slice 1990). From the superimposition of all configurations, we performed a principal component analysis (PCA)  (Dryden and Mardia 1998)  to identify the main axes of morphological variation in our dataset. This analysis identified one main principal component, which we named All-PC1 and which described 57. 8% of the variation in our dataset. The next three principal components explained approximately 10% of the variance each (Figure  S5 ). Shape variation along All-PC1 matches the interspecific shape difference: at one extreme of the All-PC1 axis, ventral branches shape are rounder and at the other extreme they are spinier.
15 Mb with a predicted number of 445 genes in the D. yakuba genome (Table  1 ). Both of these QTL have smaller phenotypic effects (8. 6% and 13. 5% of the parental species difference, respectively) than the major QTL on chromosome 3. A simple measure of spine length that relates to the lock-and-key mechanism leads to the same QTL As pointed out in a previous study of male genitalia in D. melanogaster  (McNeil et al. 2011) , principal components are specific to a given dataset because they describe the axes of variation of that particular dataset, and therefore it can be difficult to relate principal components to familiar shapes. Visual inspection of landmark configurations along the BC-PC1 axis (Figure  3 ) suggested that BC-PC1 quantifies how much the spines are elevated above the middle prominence of the ventral branches.
ACKNOWLEDGMENTS We thank  François Graner  and  Nicolas Navarro  for help with the analyses and helpful comments during preparation of the manuscript. We also thank  Raphaël Cornette  for help with landmark acquisition,  Juliette Royer  for help with dissections,  Greg Pinero  of  Janelia Scientific Computing  for updating the MSG software to handle WMD-MSG barcodes, and  Serge Picard  of  Janelia Molecular Biology Core Facility  for help troubleshooting and sequencing the WMD-MSG libraries. We also thank two anonymous reviewers for their helpful comments. The research leading to this paper has received funding from the  European Research Council  under the European Community's  Seventh Framework Program  (FP7/2007-2013 Grant Agreement no. 337579 ) and from the " Who Am I?" Laboratory of Excellence  ( ANR-11-LABX-0071 ) funded by the  French government through its "Investments for the Future" Program  operated by the  French National Research Agency (ANR)  under grant no.
Fishery data We downloaded logbook records (NOAA, 2010) of pelagic longline hauls by the U. S. fishing fleet covering a 13-year span  (October 14, 1992 through December 31, 2005)  in the region 30-55°N, 30-80°W. Each record specified the date and location of the haul and the count of each species of fish caught. We discarded potentially erroneous records-for example, records that have incomplete date information, a greater catch than the number of hooks set, or fewer than 100 hooks set. Using the remaining 62 121 records, we focused our analysis on three species of tuna (bluefin, yellowfin, and bigeye) and swordfish (Fig. 1 ). To investigate the effects of fishing gear configuration on the catch, we extracted three gear characteristics for each haul: the numbers of hooks set, light sticks used, and hooks between floats.
Eddy data We downloaded 690 weekly sea level anomaly (SLA) images (AVISO global 1/3°gridded DT-MSLA Ref merged dataset; http://www. aviso. oceanobs. com) corresponding to the time period of the fishery data. Using the Marine Geospatial Ecology Tools (MGET) software  (Roberts et al. , 2010) , we developed an automated workflow for detecting eddies in the images using the Okubo-Weiss parameter. The Okubo-Weiss parameter  (Okubo, 1970; Weiss, 1991) , W, has been used in a number of prior studies to identify eddy structures from sea surface height data (e. g. ,  Isern-Fontanet et al. , 2004 , 2006; Chelton et al. , 2007; Henson and Thomas, 2008). It is calculated using the following equation, which subtracts the square of vorticity (x 2 ) from the sum of the square of the normal component of strain (s n 2 ) and the square of the shear component of strain (s s 2 ): W ¼ s 2 n þ s 2 s À x 2 ð1Þ To calculate the normal component (s n ) and the shear component (s s ) of strain and vorticity (x), geostrophic velocities in x-direction (u) and y-direction (v) are calculated using sea level anomaly (h 0 ), acceleration due to gravity (g), and Coriolis parameter (f): u ¼ À g f @h 0 @y and m ¼ g f @h 0 @x ð2Þ Then the strain components and vorticity are calculated: s n ¼ @u @x À @m @y and s s ¼ @m @x þ @u @y and x ¼ @m @x À @u @y ð3Þ According to Eqn (1), the eddy core, which has a relatively high vorticity and low strain rates, is characterized by a large negative value of the Okubo-Weiss parameter.
In contrast, the eddy ring, which experiences relatively high rates of strain and low vorticity, has a large positive value of the parameter. The MGET software labels eddy cores in a sea level anomaly image by calculating the Okubo-Weiss parameter for each cell of the image and then identifying coherent clusters of cells that are below the threshold value of the parameter. This threshold may be expressed as an absolute value of the parameter or as a difference, in standard deviations, from the mean value of the parameter for all of the cells. To be consistent with previous eddy identification studies  (Isern-Fontanet et al. , 2006; Henson and Thomas, 2008) , we used a threshold of À0. 2 standard deviations. Because hydrodynamic features such as Gulf Stream meanders can be mistakenly flagged as eddies by the Okubo-Weiss approach, MGET tracks eddies across successive images and provides mechanisms to filter the eddy core output according to eddy size, circularity, and duration.
Sea surface temperature (SST) data Prior research has shown that our focal species exhibit different tolerances for and adaptations to water temperature  (Carey, 1982; Block et al. , 2001; Bertrand et al. , 2002a; Lawson et al. , 2010; Dewar et al. , 2011). To examine the possible influence of water temperature on the catch of these species and to provide a second environmental covariate to which eddies could be compared, we obtained SST estimates for the longline hauls extracted from the 8-day nighttime 4-km AVHRR Pathfinder SST version 4. 0 dataset (http://po daac. jpl. nasa. gov). SST at the set position was recorded for each set made within an 8-day period covered by any given satellite image. Due to cloud contamination, we were unable to obtain SST data for 6906 of our 62 121 logbook records.
, 2006; Mugo et al. , 2010; Teo and Block, 2010). To compare the number of fish caught in the anticyclonic eddy, cyclonic eddy, and non-eddy hauls, a categorical variable EDDY was constructed based on the eddy category of haul (i. e. , anticyclonic, cyclonic, or noneddy). To account for any unobserved events associated with a particular time of the year that might influence the number of fish caught, the year and month when each haul was made were included in our statistical models as two categorical variables YR and M. A detailed description of the predictor variables of our models was provided in Table  1. In the statistical models, the predicted number of fish caught (y i ) was estimated: logðy i Þ ¼ b 0 þ X 2005 j¼1993 b yr YR j þ X 12 k¼1 b m M k þ X 3 l¼1 b EDDY EDDY l þ b SST SST i þ b L L i þ b HBF HBF i þ b LAT LAT i þ b LON LON i þ logðH i Þ þ e i ð4Þ Our fishery data are count data (the number of fish caught), are overdispersed (the sample variance exceeds the sample mean), and contain a large number of zeros (hauls with no catch).
Detailed descriptions of the ZINB model can be found in  Zuur et al. (2009). We built one ZINB regression model for each species. For each species, we first built an initial ZINB model by fitting the full model (Eqn 4) in both the count portion and the zero-inflation portion of the model. To examine the relative importance of the predictor variables in the initial model, we conducted a   S1 -S4). The work was performed in the R statistical program (R Development Core Team, 2010) using the 'PSCL' package and the 'MASS' package. We reported the results using the count portion of our ZINB models. We reported a pattern for a species (i. e. , that it responds to a certain variable in the ZINB regression models) if the variable has a coefficient significant at a 95% confidence level. By comparing which eddy category has the higher predicted number of fish caught, we summarized the responses of tuna and swordfish catch toward eddy presence and polarity.
Spatial patterns in fish catch The analysis of the NOAA logbook dataset reveals the catch distribution of our four focal species in the U. S. Atlantic longline fishery (Fig. 1 ). Bluefin and bigeye tuna catches are distributed evenly in the main longline fishing regions across the North Atlantic Ocean. In contrast, yellowfin tuna and swordfish catches are more spatially aggregated. As a tropical tuna species with the narrowest tolerance for ambient water temperature among our focal species  (Bertrand et al. , 2002a) , yellowfin tuna were largely caught at lower latitudes along the East Coast of the United States. In the North Atlantic, high swordfish catch appears in two areas. One high-catch area is located within a U. S. Atlantic longline fishing region known as the NED (Northeast Distant), which occupies the area 35-55°N, 20-60°W.
Variable Bluefin tuna Yellowfin tuna Bigeye tuna Swordfish environments of low temperature  (Carey, 1982)  and to thereby exploit resources at higher latitudes  (Nakamura, 1985)  and at great depths  (Chancollon et al. , 2006). Neilson et al. (2009)  showed that swordfish display a consistent pattern of seasonal migration, moving between the temperate waters north of 40°N and the Caribbean Sea. Swordfish usually reach the temperate waters in early June, stay until October, and then reside in the Caribbean Sea from early January through to April. Kot et al. (2010)  analyzed the same logbook dataset and found swordfish catch in the NED increases in the fall. A swordfish tagging study shows that large females feed in cooler waters in higher latitudes in the NED during the summer and fall whereas smaller males stay in the lower latitudes  (Sperling et al.