dataesr/scientific-paragraphs-categorization huggingface dataset:
Acknowledgements This work was supported by STFC in the UK; at Southwest Research Institute by NASA contract NASW-00003 in the USA; the Swedish National Space Board in Sweden. The authors thank Neville Shane for the analysis and plotting software. The ASPERA-4 team are grateful to NASA for allowing the ASPERA-3 ELS flight spare to be flown aboard VEx as part of ASPERA-4. It was constructed under contract NASW-00003. The authors also wish to thank Tielong Zhang for the use of magnetometer data to produce the pitch angles for this study.
Bidimensional techniques use photography or videography to measure distance between facial points at rest and during movement  [15] [16] [17] [18] [19] [20] [21] [22] [23] [24]. Three-dimensional assessments have used automation technologies and sophisticated algorithms, often to the cost of time-consumption, expensive equipment, and uneasy applicability to daily practice  [25] [26] [27] [28]. A quantitative facial movement assessment tool that would be easy-to-reproduce, fast, free, accurate, and reliable for a sufficient number of muscles remains an unmet need. Such a tool might help clinicians to quantify facial paresis at onset, during follow-up, and after interventions such as medical, surgical, and rehabilitative programs. In the present study we have used the free and open-source software Kinovea and selected specific facial markers to quantify movements of six key muscles.
straightforward functionalities are targeted to both movement science specialists and clinicians such as physical, occupational, or speech therapists. From plain video-recordings of movements, the software allows measuring distances and times, manually or using semiautomated tracking to follow points and check live values or trajectories. To our knowledge, Kinovea has not been used for facial analysis to date. 2. 4. Assessment Procedure. All videos were analyzed using manual importing of the videos into the Kinovea software and calibrating each video to the 5 centimeter mark painted on the forehead of each subject (Figure  1 (a)). A vertical midline The time to draw markers and perform each video acquisition was recorded, as well as the time to perform analysis using Kinovea. Measurements were taken on both sides of the face.
Discussion Despite substantial research on facial motion evaluation for the past decades, no single outcome instrument has become common practice among surgical or rehabilitation teams  [14, 15, [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27] [28] [32] [33] [34] [35]. This study shows that the first version of a method using the free and open-source Kinovea software applied without any prior formal training on plain videorecordings of facial movements was reliable for frontalis measurements. For zygomaticus and orbicularis oculi, reliability was suboptimal but might be expected to improve when examined in subjects with facial paresis because of higher intersubject variability in that population (see below). For the other tested muscles (corrugator, orbicularis oris, and buccinator), reliability was unacceptable with the current paradigm.
Comparison with Other Tracking Systems. In comparison with the available literature on bidimensional analysis, the presently described technique is free, open-source, fast to use, and presents with interesting advantages. In the system used by Hadlock and Urban  [42]  of a bidimensional Facial Assessment by Computer Evaluation (FACE) derived from Photoshop but using a MATLAB interface that allows faster analysis than the regular Photoshop technique the authors analyzed only 5 movements that were not specific of individual facial muscles and work on photographs only, as opposed to videos that we could freeze at the appropriate time of maximal muscle excursion, like with the present method. To ascertain reliability for important muscles such as orbicularis oculi, zygomaticus, and perhaps corrugator, it seems important to reevaluate these muscles, together with frontalis, in patients with peripheral facial paresis.
Conservative tracers The data that we have used are those of GLODAP database  (Key et al. , 2004) , which covers the global ocean by combining a wide set of oceanographic measurements. Measurements of different expeditions since 1990, through all oceans, were synthesized and calibrated to provide a global database. In GLODAP, there are measurements of temperature, salinity, oxygen, nitrate, and phosphate. Oxygen and nitrate lead to the conservative tracer NO while oxygen and phosphate lead to PO. Temperature and salinity are used directly as conservative tracers. Strictly speaking, because of the slight compressibility of sea water, which causes a warming due to pressure, the temperature "in situ" (archived in GLODAP) is not exactly a conservative parameter because it varies with pressure.
Depths At these latitudinal zones, one should add depth ranges. First, recall that we must consider only the data from below the mixed layer (cf. Sect. 1). For this, we used the database NDP-076  (Goyet et al. , 2000) , which gives the maximum depth of the mixed layer, for each month of the year, at any point of the world ocean (on a grid of 1 • longitude by 1 • latitude). Then, the characteristics of ocean circulation can distinguish three layers: the surface ocean, at depths ranging from 50 m down to 500 m; Figures the deep ocean, at depths deeper than 1750 m.
Linux container technologies, as represented by Docker, provide an alternative to complex and time-consuming installation processes needed for scientific software. The ease of deployment and the process isolation they enable, as well as the reproducibility they permit across environments and versions, are among the qualities that make them interesting candidates for the construction of bioinformatic infrastructures, at any scale from single workstations to high throughput computing architectures. The Docker Hub is a public registry which can be used to distribute bioinformatic software as Docker images. However, its lack of curation and its genericity make it difficult for a bioinformatics user to find the most appropriate images needed. BioShaDock is a bioinformatics-focused Docker registry, which provides a local and fully controlled environment to build and publish bioinformatic software as portable Docker images.
It provides a number of improvements over the base Docker registry on authentication and permissions management, that enable its integration in existing bioinformatic infrastructures such as computing platforms. The metadata associated with the registered images are domain-centric, including for instance concepts defined in the EDAM ontology, a shared and structured vocabulary of commonly used terms in bioinformatics. The registry also includes user defined tags to facilitate its discovery, as well as a link to the tool description in the ELIXIR registry if it already exists. If it does not, the BioShaDock registry will synchronize with the registry to create a new description in the Elixir registry, based on the BioShaDock entry metadata. This link will help users get more information on the tool such as its EDAM operations, input and output types.
The recent development of user-friendly data analysis environments like Galaxy 5 ease access for biologists and bio-analysts to bioinformatic tools. These software workbenches provide a generic web user interface for command line based scientific applications, but do not solve the tools' deployment issue. Even if the task can be submitted inside a container, it is the tool designer's responsibility to provide a readily deployable component  6  and the proportion of container based components in repositories such as the Galaxy Toolsheds 7 is currently low. Finally, traditional academic publishing and funding processes emphasize the production of software with short-term goals, these being the publication of the method and/or results. Such an environment does not favor a software engineering-oriented approach to software development  8  , and this affects directly the portability and maintainability of the software products  9.
It has been positively evaluated for data intensive computation, a recent study showing that the performance of bioinformatic workflows composed by medium or long running tasks are only very slightly affected by containerization  17. Container technology has the potential to impact audiences, developers and end-users. In the scientific field, it can effectively improve reproducibility, ease deployment and facilitate the building of software collections and search engines dedicated to a specific scientific domain or topic. For these reasons, we created the BioShaDock registry that promotes the use of container technologies in bioinformatics. The BioShaDock registry provides a web entry point to deploy, search and discover ready to use bioinformatics tools, encapsulated in Docker containers.
e. the Dockerfile and the associated source code. These can be provided by pasting directly the Dockerfile contents in the web interface, by pointing to a Git repository that contains the Dockerfile and the source code, or by pointing to the source code repository and manually providing the Dockerfile. In the case of Git repository registration, it is also possible to configure the branch and location of the Dockerfile in the repository. • additional metadata which is required to describe the contents of the image in scientific terms to its potential users. Such metadata includes for instance free tags, as well as EDAM 18 terms. Following the completion of container registration, the image construction and integration steps (Figure  2 ) are automatically run on a dedicated server. The trigger of a new build is based on Dockerfile  update or via a link (URL with an API Key), shown in the web interface when the user is the owner of the tool (created it).
The creation of a tag on the image uses the same link mechanism. Such a link can be used directly (copy/paste in the brower) or via external tools or hooks (GitHub web hooks for example). The API also provides the possibility to trigger it manually, or to tag a container (i. e. set a version). The Docker images, once built and stored in BioShaDock, can be registered in the ELIXIR registry (using some LABEL metadata in the Dockerfile). It is also possible to add a link to an existing ELIXIR registry entry. By linking its contents to and from the ELIXIR registry, BioShaDock enables the discovery of Docker images from a more generic system where users might look for a given software without specifically searching for container solutions. It hence maximizes the visibility of its images and contributes to better software dissemination.
Search and execution The Docker repository acts as a platform that facilitates the dissemination of bioinformatics tools by providing ready to use Docker images. • on a personal computer with a Linux system (Windows and Mac are supported with the Docker Toolbox), in a command line (Listing 1), directly using Docker 14 ; • on a cluster integrating a Docker scheduler front-end like GO-DOCKER (v1. 0) 16 ; • in any software implementing the CWL (Common Workflow Language) specification (draft 3) 19,20 such as Arvados 21 or Rabix (v0. 6. 5) 22 ; • in the D 4 workflow portal 23 (v0. 6); • in the Galaxy environment  6  (v15. 10); • in the cloud of the French Institute of Bioinformatics with the help of the Docker virtual machine image  24. As BioShaDock is a web application written in python (>=2.
7). It manages the container's build and metadata. It is also in charge of authenticating the user against a local Docker registry and authorizing the user to push or pull a container according to their role (admin, editor, etc. ) or rights. A user can give other users access to their repository for collaborative work in the edition page of the tool. Collaborators can have read only (for private repositories) or read/write access to the tool. The backend is based on a local instance of a Docker registry. A script extracts the metadata written by the image's maintainer (Listing 2). Listing 3. An XML container metadata description generated from the LABEL instructions by BioShaDock and used to publish the container metadata in bio. tools, the ELIXIR registry. Then, an integrated REST python client (v1.
0) manages the container indexation in bio. tools (Listing 3). The first version of the registry integrates 80 Docker images that are versioned and can be re-built when the sources are updated. A REST API enables programmatic interaction with the server. For example, it can be used by external tools to extract the list of available images for job submissions. GO-DOCKER (v1. 0) and the D 4 workflow portal (v0. 6) integrate this feature. The access to the images is public. To ensure the quality of available images, BioShaDock manages the authentication and ACL (access control list) to restrict the creation and update of its images to identified trustful contributors. The current implementation (v1. 0) enables authentication using LDAP, Google or GitHub.
Discussion The aim of BioShaDock is to contribute to the aggregation and standardization of bioinformatic tools and utilities. Maintaining ready to use validated and versioned software is key in ensuring the reproducibility needed in an open science approach. Thereby, the creation of a collection of tools embedded in Docker containers, as provided by BioShaDock, is a pragmatic solution to this major bottleneck. A number of other projects also focus on the provision of bioinformatic Docker images. BioDocker 30 is a community based initiative to encourage the use of Docker images in bioinformatics. A GitHub repository stores a list of Dockerfiles that define the construction of images for the corresponding bioinformatic tool, with an open yet controlled contribution mechanism. Bioboxes 31 is an open source project that defines guidelines to build bioinformatic tool images using compatible interfaces for images which perform the same task, independent of the underlying tool, hence favoring interoperability between tools.
It is therefore, among other characteristics, very well suited to automate tool and pipeline benchmarks. It has been applied to the assessment of different types of NGS data processing methods that concern assembly software as well as metagenomics tool. Dockstore 32 is an open platform that enables the registration of Docker images described using CWL. It integrates with a number of external services for source code and image hosting, and focuses on the provision of images that can be integrated in CWL-ready environments. BioShaDock shares with these existing efforts the use of Docker as a container technology to facilitate the distribution and integration of bioinformatic tools. However, none of these systems are designed to provide local image building and storage options. Furthermore, we believe the integration of BioShaDock with external domain-centric and platform-agnostic registries such as the ELIXIR registry will significantly raise the visibility of both the images provided and the container technology itself to the community of bioinformatic tool users.
Conclusions Computer scientists and bioinformaticians can more easily disseminate their programs and find potential users using a dedicated domain-centric Docker registry. There is a wide range of perspective uses for container registries in bioinformatics: repositories managed at a community level, based on tools embedded in containers, promote the ability to exchange and replicate data analyses. In addition, the association between workflow models, data references and containerized tools could lead to the creation of interoperable and ready to use analysis components and pipeline collections maintained by many contributors. The development of such specifications is already in progress as illustrated by the CWL (Common Workflow Language)  20  and the A-SCDFM (Autonomous Semi-Concrete Data Flow Model)  33  portable workflow formats that are natively compatible with containers.
In this case, the integration of programs in a container registry like BioShaDock and the formalization of the data processing following one of these new portable workflow specifications could simplify the creation of reproducible benchmarks, teaching material, demos and the production of use case prototypes. It could also be used by article reviewers to quickly evaluate a software. The spread of container usage in the bioinformatics community and their indexing in repositories can be a solution to capture and share a large collection of data analysis methods. A wide set of bioinformatics components available on demand could induce better data analysis by simplifying tests and benchmarks.
Acknowledgements We would like to thank the  IFB/Genouest platform  for hosting the software and the use of its cluster to build the images. We thank the  IFB-CORE team , especially  Marie Grosjean  and  Sandrine Perrin , for creating containers and for supporting the deployment of the registry coupled with the IFB cloud facility. Finally, we also thank the  BioDocker core team ,  Felipe da Veiga Leprevost ,  Yasset Perez-Riverol  and  Saulo Alves Aflitos  for collaborative efforts.
Server • BioShaDock registry: https://docker-ui. genouest. org • BioShaDock home page: http://bioshadock. genouest. org Source code • BioShaDock client and tools: https://github. com/fjrmoreews/ bioshadock_client • BioShaDock local server: https://bitbucket. org/osallou/ bioshadock • Archived source code at the time of publication (client): https://zenodo. org/record/34588 34 • Archived source code at the time of publication (server): https://zenodo. org/record/34587 35 License Apache 2. 0.
a software pipeline for building loci from short-read sequence. 2010 PubMed Abstract | Publisher Full Text | Free Full Text 32. Connor BO, Kartashov A, Yuen D, et al. : DockStore. 2015. Reference Source 33. Moreews F: Design and share data analysis workflows. Application to bioinformatics intensive treatments. Thesis, université de rennes 1. 2015. 34. Francois M, Olivier S: BioShaDock client. Zenodo. 2015. Data Source 35. Olivier S, Francois M: BioShaDock server. Zenodo. 2015. Data Source. [Online; accessed 02-Dec-2015]. Reference Source 28. Bras YL, Monjeaud C: STACKS pipeline, galaxy tool descriptor. 2010. [Online; accessed 02-Dec-2015]. Reference Source 29. Bras YL, Monjeaud C: STACKS pipeline, docker container. 2010. [Online; accessed 02-Dec-2015]. Reference Source 30. BioDocker. 2015.
These data are combined via a community climate diagram, from which we derive two statistics (Table  1 ). These statistics are calculated for the climate inferred from community composition at time t inf relative to the observed climate at time t obs and are analyzed alone (unscaled) or after controlling for differences in regional pool composition (scaled). We provide an illustrated description of the approach in Fig. 1 , as well as a complete mathematical formulation in the Materials and methods. Our community climate framework is implemented as an R package (comclim; R Development Core Team 2007) and can easily be applied to other data sets.
2001). Mean pool richness is 2522 6 1042 (mean 6 SD) species. Climate axes are defined as the annual minimum and maximum monthly temperature and precipitation. These axes are chosen for this demonstration analysis because of their broad importance for plant physiology. These axes are also somewhat correlated with each other, but we use them here primarily because of their conceptual simplicity. Climate data are obtained at three time points: averaged across the last forty years (present), within the Last Glacial Maximum (LGM), and within the Intergovernmental Panel on Climate Change's (IPCC) 2007 end-century A1B scenario (2080 CE). The LGM was chosen because it is representative for the late-Quaternary glacial-interglacial climate shifts, the strongest Quaternary climatic oscillations  (Sandel et al.
Occurrence data We obtained observations of plant occurrences through the BIEN database  (Enquist et al. 2009) , which integrates georeferenced plant observations from herbarium specimens, vegetation plot inventories, and species distributions maps for the New World (see footnote 13). All taxonomic names were standardized using the Taxonomic Name Resolution Service, 15  and all observations were geographically validated to ensure the accuracy of their reported locations. We retained noncultivated observations of only tree or liana species after assigning habit to each species from the TRY database  (Kattge et al. 2011). We then retained only observations that were not part of the plot data described below, so that the occurrence and plot data would be independent from each other. The final dataset included 14 697 species and 1 083 361 observations.
Community data We began by obtaining presence/absence data from a network of 575 0. 1-ha Gentry-style forest plots  (Gentry 1982). Every tree or liana individual in the plot with diameter at breast height greater than 2. 5 cm was surveyed and was subsequently identified to species whenever possible. These plots have limited coverage in some important areas (e. g. , Amazon basin, Pacific Northwest). Because of the potential biases inherent in morphospecies identification and because of statistical requirements to have at least as many species as climate axes in each plot, we removed from the analysis all taxa that could not be identified to species level (morphospecies), resulting in a lower effective richness. We then retained plots (1) originally containing no more than 50% morphospecies, (2) having an effective richness of at least three, and (3) with an original richness less than the species pool richness.
Climate data We obtained climate data for four bioclimatic variables: maximum temperature of warmest month (10 3 8C), minimum temperature of coolest month (10 3 8C), precipitation of wettest month (mm), and precipitation of driest month (mm). We chose these four axes because they represent the extremes of two major axes of climate variation that are relevant to plants and are also easily interpreted. Data were obtained at 2. 5-arcminute resolution for three different time periods. Present-day data came from the WorldClim data set  (Hijmans et al. 2005). Last Glacial Maximum data (21 ky ago) was generated by the Community Climate System Model (CCSM;  Braconnot et al. 2007). Future data for 2080 CE (Common Era) was generated by CCSM3 under the IPCC's 2007 SRES A1B scenario (available online).
Spatial predictors We explored several potential predictors for community climate statistics. Minimum temperature and precipitation variables were obtained from present-day WorldClim data as BIO6 and BIO14, respectively. Climate shifts were calculated as the absolute value of the difference between present-day and Last Glacial Maximum mean annual temperature and precipitation. These shifts were calculated using the sources listed in Materials and methods: Climate data. Range size for species in each local community were calculated as convex hulls and obtained from the BIEN database  (Morueta-Holme et al. 2013).
g. , as in  Gotelli et al. 2010). Rare and common species are given equal weight when computing community climate statistics, which may especially bias disequilibrium inferences. The comclim R package does allow for weighted sampling and variable definitions of species pools for each community. We suggest that realworld analyses examine the sensitivity of results to several species pool definitions  (Lessard et al. 2012b). Second, the approach assumes that the realized climate niche is a good representation of the fundamental climate niche defined by physiological tolerances (e. g. ,  Arau´jo et al. 2013). Bias in community climate deviations would occur if fundamental niches were systematically displaced in one direction relative to realized niches (e. g. , because of modulation by land use;  Clavero et al.