dataesr/scientific-paragraphs-categorization huggingface dataset:
Open dashboard: CDash ITK has a stringent system of quality control that uses a combination of unit testing, regression testing, multi-platform verification, and continuous integration. The collection of unit tests are executed nightly by computers contributed by community members around the world, and reporting to a central online dashboard that summarizes the results. This web-based dashboard system (CDash) (CDash, 2013) ensures ITK's software quality as developers world continuously make changes to the code base. Build status and regression test status are visualized in a tabular form. The dashboard is an important coordination and communication tool that empowers developers to share the results of a local test with other developers by pushing them to the online summary pages. Continuous builds triggered by patches submitted to the Gerrit code review system also give feedback to developers on the impact of recent changes.
Source code version control: Git Version control is an essential practice that must be applied to all software used to support the quest for knowledge in a reproducible research environment. A good source code version control system allows developers to easily keep track of the entire history of the software development. Researchers can reference the complete state of experimental source code, and a reviewer can reliably and easily regenerate that archived state for examination. Git is an open source, distributed version control system designed to perform with speed and efficiency. Its features include easy local branching, convenient staging areas, and multiple workflows, which are particularly useful for large open source projects like ITK. The ITK community embraced the used of Git as part of the modernization activities leading to ITKv4 in 2010.
When migrating the source code repository from CVS to Git, the history of the development was preserved and a simple Git workflow was customized for ITK developers. The adoption of Git empowered ITK developers to easily create flexible workflows of branching and merging. It also allowed developers to make commits in local repositories, and hence experiment independently with variations on the software. Despite the initial steep learning curve to learn commonly used Git commands, Git provides a superior, welcoming collaboration platform for the community. It has become the new standard for source code version control. And more importantly, great tools that are base on Git are accessible to ITK, such as the peer code review system: Gerrit Review.
However, the technical nature of code solicits greater technical capabilities of the tools and methods used to evaluate its reproducibility. While this problem is multi-faceted, some progress has been made through ITK's adoption of the Gerrit Code Review system. Gerrit is an open source project maintained by the Google Android mobile phone project. This system enables a large community of developers to inspect and comment on the code changes that are proposed for inclusion in a software system. Not only has the ITK community embraced the use of Gerrit, it has also contributed fixes and features back to the upstream Gerrit project. Gerrit is implemented as a combination of a web-based front end with a Git repository backend. Most of the interactions that developers have with Gerrit, happen with the web-based front end server.
The Gerrit server provides a mechanism to effectively evaluate code changes, obtain and test those changes locally, and perform notification and transmission of the changes and comments for authors and reviewers, as well as management of the system to accept merges. Gerrit is a technology that is built around the Git distributed version control system. With Git, contributors can independently develop and test patches that are put in topic branches created out of the ITK master branch. The patch can be shared with the community by pushing the topic branch to the Gerrit server  (Gerrit, 2013). Once in the server, the change is publicly accessible via the web-based front end. Developers can then use a webbrowser to see side-by-side differences that are easily identified with color-highlighting on a file-based diff page.
Questions and suggestions can be directed at specific sections of the code with the inline comments. Whether the code can reproducibly be built and pass tests is indicated by the reviewer with a numerical Verified score, and overall evaluation is indicated with a numerical Code Review score. With the Gerrit Code Review system, reproducibility is improved through continual refinement of the corpus of ITK knowledge, as embodied in the code repository, through experimentation as implemented in the unit tests and through peer review as exercised in the code reviews.
To empower new developers and lower the barrier of entry for new users, a web site was put in place to host and distribute a large collection of examples on various ITK classes and filters (http:// itk. org/ITKExamples). In a very focused effort to grow the ITK community, an online space called ITKBarCamp (http://insightsoftwareconsortium. github. io/ITKBarCamp-doc/) was created to train new community members on the software technologies that are essential to ITK. This space provides a combination of training materials, such as source code, documentation and video tutorials, that guide newcomers at their own pace through training activities aimed at honing their software development skills. One of the very active areas in the ITK Bar Camp is a series of participatory, short YouTube. com videos with associated documentation covering various topics related to ITK, including: 1.
Modularization Since its inception in 2000, ITK was designed as a collection of about seven core libraries and about ten third party libraries. This monolithic organization of the code led over time to very large sub-libraries, as more classes were added to the toolkit. Once the core code of ITK surpassed half-a-million lines of code, it became evident that a more modular approach was needed in order to support the future continued growth of the toolkit. Such modularization was implemented in 2010-2011 as part of the larger refactoring effort that culminated to ITKv4. As a result of the modularization, the initial monolithic code base of about 12,000 files was partitioned into more than 100 modules. And, as of Oct of 2013, ITK's main code repository contains 137 modules in total. Module dependencies (see Figure  1 ) were identified and explicitly declared in the CMake-based (CMake, 2013) configuration system.
By making the CMake-based configuration system aware of the new ITK modularization, ITK adopters became enabled to select, at configuration time, the pieces of ITK that they wanted to use in their own projects. Support for adding Remote Modules was also built the ITK modularization infrastructure. These are modules that can be coupled with a particular ITK source code installation, whose source code are distributed separately from the main ITK repository. By making it possible to easily integrate new modules, the Remote Module infrastructure enables fast dissemination of research code through ITK without increasing the size of the main repository. These Remote Modules can be exchanged across members of the community based upon interest and need, which accelerates the rate of dissemination of emerging modules as well as helping those modules mature faster through the rapid use, testing, and commentary by early adopters.
Currently there are four ITK remote modules hosted on GitHub. The modularization effort significantly improves the extensibility of the toolkit and lowered the barrier of contribution. A recommended contribution process is illustrated in Figure  2. All new modules can be dropped into the ITK source tree as external modules for testing, and they should be submitted to Insight Journal for review. Once the External module passes dashboard testing and peer review, it can be submitted as a Remote module. As a result, it will be disseminated from ITK itself while its source code is maintained independent of the main ITK repository. Specifically, the source code of a Remote module can be downloaded via CMake at configuration time, which makes it a convenient method to distribute modular source code without increasing the size of the main repository.
SOFTWARE QUALITY ASSURANCE When integrating the contributions of a large number of community members, it is vital to have in place a quality control mechanism. This eliminate defects in the code before they are introduced into the repository, and it also ensures consistency and coherency across the toolkit. The quality assurance infrastructure of ITK is implemented by a combination of several interacting tools. In particular: the configuration system CMake, the online quality control dashboard CDash, the revision control system Git, and the code review system Gerrit. This infrastructure is complemented by coordination and communication tools such as mailing lists, wikis, weekly phone calls, and regular online videoconference meetings. The life cycle of a code change starts in the communication channels when community members raise issues in the code.
These issues might relate to lack of correctness, lack of desirable features, run-time performance bottlenecks, lack of support for specific platforms, or problems with specific types of data. Typically, the issues are discussed among community members until it is determined that changes in the code are required, and that one or several community members will take on the task of implementing the required changes. Once community members prepare initial versions of the required changes, they commit them in their local clones of the Git repository, from where they can submit them to the peer code review tool, Gerrit. A group of two or three reviewers are invited to review and test the code contributions and a conversation ensues, where the submitters and reviewers iron out any limitations or imperfections in the suggested changes.
During this exchanges, test builds are generated by an automated, cross-platform testing system, which are submitted to the CDash dashboard where they are publicly available for inspection. When the reviewers and submitters reach an agreement on the final version of the changes, they are merged into the official repository and are incorporated into the code base for the upcoming release. This process is supported by stringent testing requirements. In ITK, it is expected that every C++ class should have its own unit test and that algorithmic combinations should have additional tests. As a consequence, ITK carries about 2400 unit tests, which result in a code coverage higher than 84%.
OPEN SCIENCE PUBLICATION AND REVIEW In the 3 years that the community has applied the Gerrit code review system, 4005 changes have been submitted to the Gerrit review server and 6122 reviews were performed. As a matter of policy, all merged changes should have at least one review, but the number of iterations on a change varies flexibly. This results in a roughly negative exponential distribution in revisions, as evident in the histogram of revisions in Figure  4. The highest number of reviews for a single change was 38. Two direct but notable conclusions follow from this data. First, at least one other person examined and reproduced a proposed change. This certainly exceeds the publication systems where code is never disseminated. And, it likely exceeds validation systems where the code is published, but there are not incentives or checks that reviewers looked at or applied the code.
To address these challenges, computations in ITK are performed in physical space, which accounts for anisotropic spacing, etc. , and are written in such a way that they apply to N-dimensional datasets. Arbitrary pixel types are supported, and a number of standard and medical imaging specific file formats for 2D, 3D, and ND datasets are supported, including BioRad, BMP, CSV, DICOM via the GDCM or DCMTK libraries, Analyze, HDF5, JPEG, LSM, MetaIO, MGH, MINC2, MRC, MGH, NIFTI, NRRD, PhilipsREC, PNG, Stimulate, TIFF, SCIFIO, and VTK. The skull-stripping solution is initialized by registration of the target patient data set against an atlas that has a brain segmentation. In this case, the publically available XIX atlas  (Hammers et al. , 2003; Gousias et al. , 2008; Bauer et al. , 2011a)  is applied.
These properties are the result of policies and practices that encourage their emergence. The adoption of the Gerrit Code Review tool places an explicit emphasis on code review. Registration for an account is publicly available, and the default permissions allow community members to not only submit changes but also perform reviews. All community members,    9. A surface rendering is displayed along with axial, sagittal, and coronal slices. These images were rendered with 3D Slicer  (Fedorov et al. , 2012). including the novice ones, are also highly encouraged to perform reviews in project documentation, which promotes the culture of open code review. Second, the policy of requiring a positive code review, even for the most experienced community members, encourages continuous of even senior members while promoting civility.
It obstructionism and encourages collaboration by promoting reciprocal constructive criticism. While the Gerrit Code Review system provides fine-grained and thorough review of code as it enters into the Toolkit, the Insight Journal plays an important role in the high-level introduction and presentation of new algorithms that are often relevant to the Toolkit. While the medical imaging and broader scientific community have recently embraced the open science movement that champions reproducible research through online open access to articles, open source code, and open data, the Insight Journal has had these qualities for many years, and there are lessons to be learned from its experiences. As indicated by the high number of views and downloads, the utility and value of articles published in the Insight Journal is high.
Unlike traditional journal articles that are accessible only to those privileged with a subscription to penetrate their paywall, articles in are available to any researcher with an internet connection. Once the article, code, and data are obtained, they offer much more value to a researcher. Instead of only gathering a small level of information from a suspiciously reported text, the researcher can verify the results, understand the "devilish details" elucidated by the source code, and use both the code and data as a starting point for new avenues of research. Additionally, the researcher may not be interested in the theory underlying the reported algorithm at all-the implementation provides a concrete solution to address a tangential problem at hand. Indeed, algorithms and solutions published in many academic journals are heralded for their ability to address important problems, but solutions often never see translation into pragmatic application because they are irreproducible and false or because they lack an easy to apply manifestation in quality, clean code.
However, there is no complete, transient solution to this problem; the scientific community must recognize that reproducible science requires time, effort, and resources. While an open source software image analysis library like ITK is necessary for open science, it is insufficient to truly enable reproducibility. Data and documentation are also required  (Carp, 2012; Vines et al. , 2013; Glasziou et al. , 2014). Acquisition and location of data is difficult even with the sparse publically available datasets, but accessible indexes such as the Datastore Module in 3D Slicer  (Fedorov et al. , 2012)   The technological challenges of reproducibility have all been solved. We now require a cultural change by which we must make it unacceptable that any article in the domain of medical image analysis be published without a full set of source code, data, and parameters that will enable an independent group to replicate the process and verify or refute the findings.
ACKNOWLEDGMENTS We would like to thank the  Insight Toolkit community , which we are honored to participant in. We would like to thank  Dr. Ana Nelson  for her training in the Dexy (http://www. dexy. it/)reproducible research documentation tool for this article. The data, code, and sources to build this article can be found at https://github. com /InsightSoftwareConsortium/ITKFrontierNeuroinformatics2013.
4 ). We found a total of 52 SNPs and 2 indel polymorphisms (V337 and V327) (table  3 ). Seven of these SNPs (V270, V274, V275, V300, V329, V332, and V335) correspond to SNPs, which are present in the build 137 of the Single Nucleotide Polymorphism database (dbSNP). The nucleotide diversity for the entire region was estimated as = 2. 3 ± 0. 2 Â 10 À4 , a slightly higher value than previously observed for the MSY  (Shen et al. 2000; Trombetta et al. 2010). If gene conversion is operating between X-Y human sequences, we would expect 1) that the number of Y SNPs found in X-Y GSV sites is higher than expected by chance and 2) that the Y-linked derived allele (as inferred from the Y phylogeny) is the same as the gametologous site on the X. The observation of recurrent mutation in the Y phylogeny and shared X-Y polymorphisms are additional common outcomes of gametologous gene conversion.
Interspecies Data Analysis An approximately 1-Mb human X-linked region (ChrX: 2699520-3689259), which corresponds to the youngest evolutionary stratum (stratum 5 sensu  Ross et al. [2005] , which corresponds to stratum 9 following the criteria of  Pandey et al. [2013] ), was extracted from the X chromosome sequence reported in the February 2009 assembly of the UCSC Genome Browser (http://genome. ucsc. edu/, last accessed May 20, 2014). Afterwards, the whole region was divided into 5-kb windows (50% overlapping) to search for paralogous and orthologous regions in humans (GRCh37/ hg19) and chimpanzees (CGSG 2. 1. 3), using the BLAT function of the UCSC genome browser. The four-way alignment (X and Y chromosomes, both from humans and chimpanzees) was performed using the ClustalW2 software (http://www.
ebi. ac. uk/Tools/clustalw2/, last accessed May 20, 2014;  Larkin et al. 2007). A portion of the alignment was eliminated from further analysis if 1) one species showed a nonspecified sequence (i. e. , a stretch of N), and/or 2) one species showed a larger gap than 100 bp (due to insertion/deletions difference among the four sequences). Variable sites within the alignment have been identified using the DNAsp ver. 4. 50. 1 software  (Rozas et al. 2003). A CER was defined as a region of the alignment with four or more C-sites per kb. The start and end of each CER were considered to be the nucleotide positions of the two outermost C-sites observed in that CER. For S-site and N-site counting, we arbitrarily chose to consider a deletion of more than 10 bp as a single site, which is generated by a single event.
An approximately 150-bp deletion in the human X chromosome was considered for the alignment of CER14, due to the large number of C-sites within the alignment. The P value for the observed number of CERs was obtained using a random permutation test. More specifically, we randomized the distribution for the 266 observed C-sites within 456,564 bp of alignment (using the "randbetween" function of Microsoft Excel software), and counted the region with four or more C-sites/kb. This process was replicated 1,000 times, which produced the null distribution of CERs (supplementary fig. S1 , Supplementary Material online). We determined the P value of observing !19 CERs assuming that their null distribution follows a Poisson distribution. The expected number of C-sites is given by the formula: K ¼ L Â P 2 0 2 , where L is the length of the alignment (in bp) and P 0 is the number of nucleotide substitutions per site between the orthologous sequences as indicated in  Osada and Innan (2008).
The P values resulting from the test for gene conversion (table 2) were calculated according to  Osada and Innan (2008) , using the formula: P ¼ 1 À X kÀ1 i¼0 exp À k À Á k i i! , in which K is the observed number of C-sites. The PC value (Kijima and Innan 2010) was calculated using the following equation: PC = nC/(nC + nN), where nC and nN are the numbers of type-C and type-N sites, respectively. Interspecific NJ trees and Split Decomposition networks were constructed using the SplitsTree program  (Huson and Bryant 2006).
Principle of the experiment The objective of the experiment is to validate CAMELOT experimentally on a perturbed laser beam. The experiment is performed using a low power continuous fibered laser whose complex amplitude is modulated to create spatial perturbations. The experimental setup is illustrated on Fig. 2. The spatial modulation of the laser beam (phase and modulus) is controlled by a field control module and conjugated with the clear aperture plane. The beam going through the clear aperture crosses a beam splitter and is focused on the CAMELOT camera of Fig. 1. The camera is used to record three near focal plane intensity distributions, which are given as inputs to CAMELOT. The field estimated by the latter will be CAMELOT's estimation of the complex field is cross-validated in the two following ways: regarding field modulus, A C is compared with A M , called measured modulus of the field hereafter, which is computed as the square-root of image I M : A M = √ I M.
Results The three focal plane images are now used in the CAMELOT estimation. The minimization process is initiated with a homogeneous modulus field, a phase set to zero and differential tip/tilts also set to zero. The number of estimated points in the estimation plane is N 0 = 64 × 64. The current implementation of the algorithm is written in the IDL language. A relevant measurement of the success of our inversion method is the quality of the match between the data i k and the model that can be computed from the estimated field through Eq  (6). This latter map is presented on the middle row of Fig. 5. The moduli of the differences between the measurements and the direct model, that is to say estimation residuals, are displayed on the bottom row of the same figure. These residuals are below 1% of the maximum of the measurements on the three planes.
Background: Grapevine (Vitis vinifera L. ) is one of the most important fruit crops in the world and serves as a valuable model for fruit development in woody species. A major breakthrough in grapevine genomics was achieved in 2007 with the sequencing of the Vitis vinifera cv. PN40024 genome. Subsequently, data on structural and functional characterization of grape genes accumulated exponentially. To better exploit the results obtained by the international community, we think that a coordinated nomenclature for gene naming in species with sequenced genomes is essential. It will pave the way for the accumulation of functional data that will enable effective scientific discussion and discovery. The exploitation of data that were generated independently of the genome release is hampered by their heterogeneous nature and by often incompatible and decentralized storage.
Background As for many other major model plant species, the release of the grapevine genome in 2007  [1]  led to a rapid accumulation of "Omics"-scale data and a burst of highthroughput studies. In 2010, the V. vinifera cv. PN40024 genome sequence was updated from 8X to 12X coverage  [2]  and is, to date, the reference genome for V. vinifera. The gene models and their putative functions have been automatically predicted from the genome sequence and have been used in many functional studies. The results from these published studies were deposited in generalpurpose gene databases such as NCBI, but also in other independent repositories. These data are a highly informative resource to help curate the automatic prediction. Another resource, consisting of manually curated gene families associated with heterogeneous levels of functional evidence is also growing rapidly  [3] [4] [5] [6]  but lacks a central storage system allowing coordination of gene nomenclature.