dataesr/scientific-paragraphs-categorization huggingface dataset:
There is, on the other hand, already open licensing for databases with the aim of guaranteeing the future availability of information. For example there are many different kinds of databases that use the 'science commons' license, like the major protein database called the neurocommons and the hapmap e. The main aim of these this type of open licensing is to avoid that other intellectual property claims impede the access to the data by other users and to facilitate this usage (see  Gitter 2007). The availability of the information is integral to the interaction of researchers with a wide range of datasets being used in the different sub-disciplines of the life sciences. This includes many examples of open source projects with users around the world like the the bio-versions of programming languages like are bio-java, bio-perl, bio-spice, bio-harvester and bio-lisp.
Each of these has its own voluntary or public science support communities like the Open Bioinformatics Foundation. Elsewhere there are numerous projects in bio-informatics that aim for a wide range of open source software tools for the analysis of data. For example there is Bio-SPICE a software tool set that is specially designed for research into the modeling and simulation of 'spatio-temporal processes in living cells'. Its website describes Bio-SPICE by writing: "Bio-SPICE, an open source framework and software tool-set for Systems Biology" f. Finally, these programs illustrate the significance of the ability to rewrite software programs for asking novel biological questions, which is necessary in many different kinds of experiments. The point is therefore not only that there are some software programs in the life sciences that are open source whereas most are not; open source is integral to forms of experimentation that depends on constant coordination of a 'multiplicity of techniques coordinated on an elevated surface (the screen)' (see  Mackenzie 2010: 189).
Crucially, the introduction of open source in the life sciences is therefore not a feature of patenting or the proliferation of exclusivity. This is an alternative that takes its shape at the intersection with informatics and as a necessary part of questioning the biological, which is too limited in the case of preprogrammed homology searches of databases or software programs that already make information available in the public domain. This difference is significant: it implies a changing relationship between 'openness' and exclusivity that runs throughout the the life sciences and that requires increasingly wide variety of researchers with highly specialized knowledge and different levels of commitment to work together.
Common-based strategies Freely available source code and the ability to rewrite the programming of a particular kind of software or database are significant as examples of the re-definition of openness in the life sciences. The various examples that were mentioned are integral to the translation of DNA into informatic formats and can be followed to much more complex research settings wherein massive amounts of data need to be computed and interpreted. Again there are various kinds of open source models involved, which show how the rise of bio-informatics implies that the sharing of information has become a norm and how open source is one of the more efficient way of processing information about life as essentially complex and interactive. Consider a project called 'folding@home', which is one of Yochai Benkler's counter examples to the suggestion that drug development is too complex, expensive and time consuming for non-commercial projects based on 'common-based strategies' (see  Benkler 2006: 83, 351).
What is special about this project is that it models the folding of three-dimensional proteins that might cause diseases on the basis of "voluntary" contributions. Its calculations are made by involving millions of volunteers in a internet-based distributed computation projects (ibid). The contributors do nothing more than to install a small client program that runs on unused capacities of their PCs, which enables packages of data to be sent periodically. However the project is not open source; folding@home does not allow for modification of the programming and redistribution of the data by its collaborators, which raises the question whether there is a difference with 'common-based strategies' that do have these characteristics. Folding@home became faster than any single supercomputer in 2007, surpassing the petabytes and tetraflops of Blue Gene, a supercomputer build by IBM that is specially developed for experiments in the life sciences  (Noble 2006: 65-67).
These types of computation are needed to create situations and models of life, which requires the processing of enormous amounts of information. Notably Blue Gene runs on Linux, which is 'the key working example' of open source as 'a collective project that has been shared and worked on freely'  (Berry 2004: 80). Therefore supercomputing and hardware are an integral part of the introduction of open source in the life sciences and signal that its scope includes the various types of computational modeling within experimental set-ups. For example the Blue Brain Project, is one of several wherein Blue Gene is used: its objective is to 'to reverse engineer the human brain and recreate it at the cellular level inside a computer simulation' with the aim to develop brain disease treatments g. The experimental side to the convergence with informatics foregrounds the specificity of the interface between the life sciences and informatics.
The reason, however, that this is a significant example is an indicator at the overlap between open source programming in the life sciences and synthetic biology. This field has quickly become exemplary for its application of open source principles to genetic engineering. So far the discussion has outline the varies types of examples of open source in the life sciences, showing that the alternative to patenting takes a shape that is directly equivalent to open source in informatics. Accordingly the patenting of DNA is not the primary setting for examining the sharing of resources among life scientists; there are various fields that are engaging directly with informatics, which includes open source programming, databases and hardware as integral parts of experimental set-ups. The implication is not that there will be no patents or the various controversies will be settled; these are likely to intensify as more legal work will be needed to separate DNA as a composition of matter from DNA as an informatic entity.
The exchange of synthetic DNA as a business model Open source in synthetic biology subscribes to the underlying value of the patent system, which is the acceleration of the development and introduction of novel technologies (see  Hilgartner 2012:192). In addition the exchange of synthetic DNA resembles the formation of informatic markets and informatic commodities. Consider Blue Gene again: this time as a project that combines an implication of Linux with the business model of IBM. Open source models have become so successful in informatics that they are by now an integral part of the competitive strategy of many of its major companies. Even though Linux represents the greatest achievement that open source has been capable of, it is with ease that it has been 'slotted into corporate software production at companies such as IBM, Compaq, Hewlett Packerd, Apple, and Sun Microsystems'  (Mackenzie 2005: 72).
What this suggests is that the scope of collaboration in the life sciences is at once an experimental setting, focused on the complexity of the modeling of cells, and a feature of the formation of markets for sophisticated hardware, software and other informatic artifacts that are being used throughout the life sciences. This includes the business model of synthetic biology, which is informatic in the sense that the freely available information in databases can be re-materialized into experimental samples that are improved, optimized or altered in respect of one or more of its properties. Specifically the business model of synthetic biology revolves around the supply of genes and genomes that are standardized on demand. Scientists could do this kind of synthesis themselves but this would be time-consuming and the prices of DNA synthesis are dropping fast.
As an approach, however, the focus on engineering pathways and the open source model come together as an approach that seeks to create a design process that requires a much larger involvement and scope for collaboration. This feature of the open source model will be discussed below. The main argument is that the involvement of expects, policy-makers and students is limited by comparison with open source models in informatics. Following a critical discussion of open source as a response to societal concerns over the risks of synthetic biology, a comparison will be drawn with the reputation of of open source for stability an reliability. Considering how the Linux operating system gets much of its reputation from crashing much less than its proprietary counterparts, is it not the least to expect from open source in synthetic biology that there is complete openness about the exaggeration of its capacities? Open source and the users of f synthetic DNA Bio-safety and bio-security governance are topics that are high on the agenda of policymakers.
The limitations to collaboration in the life sciences are not only a question of lowering the threshold to participate in experimentation. The limitations of open source as an approach to working with synthetic DNA are illustrated in the image below, which shows an alternative scenario for open source in the life sciences based on language that is loosely derived from the layout of the website of Ubuntu and has been adapted with Photoshop. Ubuntu is one of the most used examples of the numerous 'community developed' Linuxbased operating systems m. The picture shows an adaption of one of the pages of its website and illustrates the possibilities for an alternative for what 'free' and 'open' signify in the life sciences. Also its text highlights the importance of users in open source projects in informatics, which is also prominent in synthetic biology; its purpose, however, is to show the limitations of an open source approach whose users range from students who are able to contribute to the design of genetic constructs and synthetic biologists who act as regulators (Figure  2 ).
Comparing the BioBricks Foundation with openness as implemented in the various distributions of Linux such as Ubuntu shows that the former has very few possibilities types of involvement that would lead to different kinds of priorities about what is a stable release and what is not. Certainly the model in synthetic biology and for open source in the life sciences involves different kinds of experts to collaborate and set new standards by sharing information and making their inventions available. It even involves open licensing. However synthetic biology exaggerates its ability to 'cut' and 'paste' biological modules with rigidly standardized properties while its notion of 'the user' as innovator is minimal when comparing the design process in synthetic biology with open source in informatics.
Towards open source as an alternative There are some signs that there might be a considerable potential for open source models in the life sciences that are not being realized at this moment, neither in synthetic biology or elsewhere. The potential for a more principled alternative can be brought into focus by exploring the societal implications of a overtly pragmatic analogy with open source. Subsequently it becomes possible to suggest a re-orientation, which is to say that it is to soon too present a fully formed comparison to Linux or that has a clear relationship to the many societal concerns in play. Firstly, the main argument of this paper is twofold: (1) there is a convergence with open source in informatics that ranges from bio-informatics to synthetic biology and (2) the kind of alternative that is taking shape remains limited.
Are there, however, any reasons to presume that there could not be another kind of analogy? What about seeking to mirror the discussion so far: to aim for an analogy with open source that seeks to follow the example set by the FSF in creating a bottom-up, transparent and decentralized movement that eventually might show the kind of innovation that is possible in a design process that does not exclude the priorities of any of its users? When open licensing was first introduced, it was not clear whether this was legal and there were few companies and governments that were interested. Only in early nineties there began to be widespread recognition of Linux' capacity to innovate. In the decade that followed the success would result in a movement that had made powerful allies who had a strong interest in undermining Microsoft's position in the software market (see  Mackenzie 2005; Deibel 2009 ).
There is, however, very little to indicate that the kind of operating system being designed in synthetic biology is comparable to the safety and stability that Ubuntu and Linux are known for. The statement signals at the kind of direction that can be expected from a business model of synthetic biology as a re-materialization of the representation of DNA in a 'purely informational form'as data or image -[that] will be 'standing in for particular materials resources henceforth to be absent'  (Parry 2004: 65). What this kind of comparison with open source in informatics overlooks is again how DNA synthesis is a messy and unpredictable process. As the text from the Ubuntu-website that was shown above has warned, it is necessary to be extremely careful in the design of a new version of Ubuntu because: 'regression in a stable release is a catastrophe'.
This limits the appeal of BIOS; such a project ends up appearing inefficient (in line with the rhetoric of the open source movement) in the absence of a more principled position as a generalized response to the commodification of DNA (analogous to the response to the commodification of source code by the FSF). What is missing in BIOS, as with the BioBricks GPL and Endy's operating system for wheat, are the kind of 'bugs' that cannot be solved by genetic engineering and that require a much more collaborative view of plant breeding. The alternative to 'horrendously closed code' is than to be able to adapt crops to viruses, particular soil types, specific climatic conditions and so forth. To follow BIOS' licensing in this direction might begin by copylefting datasets and genetic circuitries so that their usage is conditional on the support of farmers' rights (see  Kipp 2005 , Aoki 2008 , Shrinivas 2006a , 2006b).
It is in experimental settings of this kind that the convergence with informatics is distinguishable from a juridical orientation on patenting and possible alternatives. On the other hand the introduction of open source in the life sciences is in the majority of instances an alternative that is only about the efficiency and capacity of collaboration in programming and in experimental set-ups. This was explained in the first part of the paper that focused on the representation of scientific objects as mediated by informatic artifacts, like supercomputer, numerous kinds of software, databases as well as well as of the most sophisticated models of cell behavior. The second part highlighted that the patenting of DNA is not that relevant for the sale and servicing of informatic artifacts, which includes synthetic biology.
The example that was used is the student competition for designing BioBricks, which suggests that synthetic biology could be something that is easy to do. The problem, however, is that open source programming is not easy, like Linux is not necessarily that easy to use. It is not the simplest approach that is important, as Linux illustrates; it is only with releases like Ubuntu that Linux has become available for anyone whereas earlier it was difficult to obtain, install and operate for those without the necessary programming skills. Ultimately the potential of open source in the life sciences is not primarily about the design process for synthetic constructs into something simple or more profitable but its potential to change the way wherein its designs are being run on living materials. Not everyone needs to understand and be involved in the most complicated projects of the life sciences but anyone can be involved in setting standards for reliability, safety and (food) security.
The Blue Brain project includes open source software development called NEURON (see The Blue Brain Project, http://www. artificialbrains. com/blue-brain-project). The oldest example of a collaborative and distributed network involved in modeling cells is probably the Japanese e-cell project from 1996  (Ewan 2004). Another example that shows the considerable institutional backing of these kinds of networks is the UCSD 'signaling gateway' , which operates by having experts monitor and update two or three molecules each and peer-review each others work on the thousands of protein molecules inside the database. These are called Molecule Pages, which are backed by the journal Nature and include interactive kinds of pre-publication and peer-review; (Signalling Gateway. http://www. signaling-gateway.
). c See http://www. gnu. org/philosophy/why-free. html (accessed May 2013) also see Moglen 2003 and Mako Hill 2005. See for example, http://www. genomesonline. org, and http://www. hupo. org/, www. ncbi. nlm. nih. gov/Genbank and www. nlm. nih. gov/news/press_releases/dna_rna_100_gig. html (accessed May 2013). e On the Neurocommons see: http://neurocommons. org/page/Main_Page. On the Hapmap see http://snp. cshl. org/thehapmap. html. en (accessed May 2013). /biospice. sourceforge. net/, http://www. open-bio. org/wiki/Main_Page, (accessed May 2013).
). c See http://www. gnu. org/philosophy/why-free. html (accessed May 2013) also see Moglen 2003 and Mako Hill 2005. See for example, http://www. genomesonline. org, and http://www. hupo. org/, www. ncbi. nlm. nih. gov/Genbank and www. nlm. nih. gov/news/press_releases/dna_rna_100_gig. html (accessed May 2013). e On the Neurocommons see: http://neurocommons. org/page/Main_Page. On the Hapmap see http://snp. cshl. org/thehapmap. html. en (accessed May 2013). /biospice. sourceforge. net/, http://www. open-bio. org/wiki/Main_Page, (accessed May 2013). d f see http:/.
Numerical experiments In the sequel, we provide numerical simulations. Classically, in the D m -spline setting, parameter balancing the semi-norm is set to 10 -6. (There also exist methods for an automatic choice of mainly based on statistical considerations as the generalized cross-validation and the generalized maximum likelihood methods (see  [18]  and  [19] ). Owing to the fact that the proposed algorithm calls basic linear algebra functions such that transposing matrices, summing matrices, multiplying matrices or solving linear systems, it appeared relevant to use LAPACK and Basic Linear Algebra Subprogram routines. BLAS is a corpus of routines that provide standard building blocks for performing basic vector and matrix operations. LAPACK (designed at the outset to exploit BLAS routines) provides routines for solving systems of linear equations among others.
For each subdomain Ω ν , ν ∈ {1, • • • , N }, we obtain two disconnected linear systems to be solved with the same matrix. Our resorting to BLAS/LAPACK thus seems apposite. The computations on each subdomain Ω ν being independent, the use of OpenMP appeared relevant. The OpenMP Application Program Interface supports multi-platform shared-memory programing in C/C++ and Fortran on all architectures (see the official website http://openmp. org/wp/). The first application involving large deformations is provided in Fig. 2  and is similar to an application given in  [20]  in the case of topology-preserving segmentation. The synthetic Reference image represents two disks. The Template image, which is defined on the same image domain (100 × 100), is made of a black ellipse such that when superimposed on the Reference image its boundary encloses the two disks (see Fig.
The Orthologous Matrix (OMA) project is a method and associated database inferring evolutionary relationships amongst currently 1706 complete proteomes (i. e. the protein sequence associated for every protein-coding gene in all genomes). In this update article, we present six major new developments in OMA: (i) a new web interface; (ii) Gene Ontology function predictions as part of the OMA pipeline; (iii) better support for plant genomes and in particular homeologs in the wheat genome; (iv) a new synteny viewer providing the genomic context of orthologs; (v) statically computed hierarchical orthologous groups subsets downloadable in OrthoXML format; and (vi) possibility to export parts of the all-against-all computations and to combine them with custom data for 'client-side' orthology prediction.
The need for orthology inference has led to the development of numerous methods (reviewed in 5) and databases, notably including EggNOG  (6) , Ensembl Compara  (7) , Inparanoid  (8) , MBGD  (9) , OrthoDB  (10) , OrthoMCL  (11) , Panther  (12) , PhylomeDB  (13) , Plaza  (14)  and OMA  (15). The OMA (Orthologous MAtrix) project is a method and database for the inference of orthologs amongst complete proteomes (i. e. the protein sequences associated for every protein-coding gene in all genomes). Initiated in 2004, OMA has undergone 17 major releases, steadily increasing the number of proteomes under consideration from 150 to 1706 across all domains of life. Besides its large scope, the distinctive features of OMA are the high specificity of the inferred orthologs (e. g. [16] [17] [18] [19] , feature-rich web interface, availability of data in a wide range of formats and interfaces and frequent update schedule of two releases per year.
GENE ONTOLOGY FUNCTION INFERENCE AS PART OF THE OMA PIPELINE One key motivation for orthology inference is to computationally predict the roles that genes play in living organisms--e. g. Cellular Component, Molecular Function and Biological Process of the Gene Ontology  (23). For many years, Gene Ontology (GO) annotations from the UniProt-GOA database  (24)  have been linked to all sequences in OMA. Additionally, we now provide inferred annotations based on orthology relationships: within the orthologous groups, we propagate GO annotations across different species. To infer GO annotations, we start with curated annotations that are based on direct evidence from the literature: GO evidence codes EXP, IDA, IPI, IMP, IGI and IEP (http://geneontology. org/page/guide-go-evidence-codes). We then propagate them across OMA groups--sets of genes for which all members are inferred to be mutually orthologous--as these have been previously shown to be highly coherent in terms of functional annotations  (25).
Indeed, the propagated annotation complements one of the known annotations for the A. thaliana protein, 'embryo sac development'. Overall, the OMA database now provides 442 376 477 function annotations for 7 947 728 proteins (Figure  3 ). Amongst the available annotations, most are computationally inferred; our own predictions constitute about 20% of the available annotations. Function annotations based on OMA orthologs are particularly valuable for proteins for which other computational annotation methods provide no annotations and the available annotations assigned by curators are relatively general and/or sparse. In the most recent OMA release, we provide annotations for 423 983 proteins for which there are no other electronic annotations. For example, at the time of writing the A. thaliana protein with UniProt identifier Q8VYZ5 had no electronically inferred GO annotations (evidence code IEA); it had five annotations based on evidence codes ISS or RCA, which are not used in our propagation pipeline; and the annotations from literature-based evidence were 'nucleolus' (IDA), 'rRNA processing' (IMP) and 'embryo sac development' (IMP).