dataesr/scientific-paragraphs-categorization huggingface dataset:
Classification yields concepts and taxa that represent scientific hypotheses; placing those models into an ontology is beyond the scope of this paper, but a discussion of the applicability of ontologies to taxonomy can be found in  [28]. New entities -for example, digital images or tissue subsamplescan be derived from museum specimens. This derivation may involve procedures that are destructive, as in the case of tissue harvesting for DNA extraction. In many cases, subsamples find their way into other types of collections, such as cryo-facilities, that are often housed and databased independently from the source collection. Comprehensive biodiversity surveys, such as the Moorea Biocode Project (described in more detail in the Discussion), along with many smaller-scale projects, can be enhanced by the ability to track objects and data across multiple resources and communicate relationships derived from specimen subsampling and distribution to multiple physical or digital repositories.
In contrast to museum collections, many ecological studies are based on observations or measurements taken from samples that are neither collected nor archived. Like environmental samples, survey targets may exhibit nested relationships with other features, such as a leaf coming from a plant located in a subplot within a plot (Figure  1B ). As a result of this spatial nesting, environmental variables associated with a plot may also be associated with a leaf collected within that plot and with the DNA extracted from that leaf. This type of nested observation or sampling requires the same sort of metadata tracking through a chain of events that was described above for museum and environmental sampling. Database implementations, such as TRY  [31]  or BIEN  [32] , use relational databases to successfully model the complexity of ecological sampling, but interpretation of the tables and their attributes is limited to the internal schema of these databases.
Community Development Processes and Current State of Biodiversity Standards Although there is still no widely accepted terminology or standard that spans all aspects of biodiversity sampling and observing, there is a long history of community-developed vocabularies and standards for particular aspects of biodiversity data, particularly for museum collection information. We highlight some of those efforts with an eye to how their specific limitations inspired the development of the BCO and ongoing efforts in the PCO and ENVO. The Biodiversity Information Standards (TDWG) organization is a community dedicated to the development of standards for the exchange of biological/biodiversity data. TDWG has ratified and maintains the Darwin Core (DwC)  [33]  and Access to Biological Collections Data (ABCD)  [34]  standards.
DwC is a relatively small (,200) set of terms and definitions -in the spirit of minimum information standards -that was explicitly developed with no class-property hierarchical structure. This was due to both considerations of simplicity and a lack of mature standards for expressing semantics at the time. ABCD includes terms in common with, and mapped to, DwC, but it has a hierarchical structure and many more terms (,1200). Although it aims to define the semantics of all of its terms, it is not specified as an ontology and lacks a subject-predicate-object format. Both DwC and ABCD have extensions to increase the scope of data they can cover (e. g. , DNA collections) and both have been described formally as XML schemas. DwC has been formally described in RDF (dwc-rdf) thus facilitating re-use of terms, but ABCD currently is not available in RDF.
DSW provides a semantic framework for reasoning over biodiversity data, but is limited to the context of the DwC terminology and is thus not sufficiently general to cover many of the use cases driving the development of the BCO. TDWG efforts have primarily focused on the description of objects in museum collections, with some attention to observational data, whereas the Genomic Standards Consortium (GSC)  [38]  has focused on the annotation of genetic sequence data, including those obtained from environmental samples. The GSC's standards are specified in the Minimum Information about any (x) Sequence (MIxS)  [39]  family of metadata checklists. MIxS consists of checklists for genome/metagenome sequences (MIGS/MIMS) and genetic marker sequences (MIMARKS), with shared descriptors across all three checklists, checklist-specific descriptors, and a suite of environment-specific descriptor ''packages''.
Based on that proposal, in 2011, the NSF-funded Research Coordination Network for the Genomic Standards Consortium (RCN4GSC)  [44] ,  [45]  began a series of meetings to reconcile discrepancies between terms in the DwC and the MIxS standards  [46]. This activity was meant to help harmonize vocabularies used to describe museum collections data and metagenomic biodiversity assays. The vocabulary alignment meetings recognized inconsistencies in the use of fundamental terms such as 'sample', 'specimen', and 'occurrence'. In response, the RCN4GSC organized a Semantics of Biodiversity (SoB) workshop in Lawrence, Kansas in May of 2012  [47]. SoB brought together a range of domain experts to comment on a proposal for aligning terms within a larger framework, using the Basic Formal Ontology (BFO)  [48] ,  [49]  and OBO Foundry principles as a guide.
Building on the SoB event, the Biocode Commons Ontology Hackathon, supported by the RCN4GSC and the BiSciCol project, was held at GSC14 in Oxford  [47]  to formalize the concepts outlined at the SoB workshop as an ontology. Initial investigations revealed that existing OBO Foundry ontologies, such as the Gene Ontology (GO)  [50] ,  [51] , Sequence Ontology (SO)  [52] ,  [53] , or Ontology for Biomedical Investigations (OBI)  [54]  -while providing some classes relevant to the biodiversity domain -do not model concepts like museum specimens or environmental sampling and their relationships to the entities derived from them. As a result, a decision was made to develop the BCO further as a separate ontology. At both the SoB and the Biocode Commons workshops, participants were aware that ontology development would need to support existing standards such as DwC and MIxS.
Furthermore, the NSF-funded EAGER: Interoperative Informatics Infrastructure for Biodiversity Research (I3BR; hosted at UCSD with John Wooley as PI) is building on the efforts of the RCN4GSC, TDWG, GSC, and GBIF to support the increased interoperability of molecular and biodiversity standards and syntax and thus enhance semantic interoperability of their data holdings. The BCO represents an important part of this effort, by providing the necessary semantics to model biodiversity data. Going forward, I3BR support will help create task groups to establish the infrastructure for managing ontologies.
Discussion Many applications of biodiversity science require the collection, integration, and analysis of data from a variety of sources as well as a way to link information about biological entities and their derivatives as materials and data move through various processes and institutions (Table  S1 ). Ontologies offer an opportunity to link data semantically within and across biodiversity sub-disciplines, by creating a unified knowledge model that spans many data types. The BCO -in conjunction with other ontologies such as PCO, ENVO, or OBI -helps to break down the barriers among data silos, enhancing the value of biodiversity data by allowing researchers to query across data sets. We illustrate the complexity of the problem domain and the utility of ontologies by focusing on two specific use cases drawn from the Moorea Biocode Project and the Genomic Observatories Network, as mentioned earlier, but also discuss additional examples from other contexts.
Tracking samples in a large bio-inventory project: the Moorea Biocode Project The Moorea Biocode Project aimed to create the first comprehensive inventory of all non-microbial life in a tropical ecosystem by constructing a library of genetic markers (DNA barcodes  [58] ) and physical identifiers for every species of plant, animal, and fungus on the Pacific island of Moorea  [59]. Each step in the Moorea Biocode Project, such as those shown in Figure  3A , follows protocols, has inputs and outputs, and is accompanied by metadata collection. Starting at any step in the chain, researchers need to find and access data/metadata associated with any other step. Figure  3B  shows selected ontology terms that can be used to annotate data from the Moorea Biocode Project. For the sake of clarity, Figure  3  does not show every relationship that could or should be annotated in this workflow.
One outcome of the annotation process is to enable a linked data approach  [60]  by representing relationships among instances and between instances and ontology term identifiers, using uniform resource identifiers (URIs) as globally unique identifiers. The BiSciCol project is implementing such an approach by storing relationships harvested from community-accessible data sets and enabling queries using relevant ontologies. Some examples of the types of queries that could be performed in the context of the Moorea Biocode Project use case include: 1. Show cases where the taxonomic identification determined through morphological keying (e. g. , TaxonID A in Figure  3B ) differs from that determined through DNA sequencing (e. g. , TaxonID B in Figure  3B ). 2. List the ENVO:feature and other environmental parameters recorded during a Moorea Biocode sampling process that are associated with Genbank sequence B.
Coordinating multi-site environmental sampling: Genomic Observatories Network The Genomics Observatories Network aims to build a global network of research sites, each of which collect and integrate genomic, environmental, and socio-ecological data -all well contextualized by the time and place of collection  [9] ,  [30]. Genomic Observatories may be terrestrial, freshwater, or marine, and should support intensive environmental and ecological data collection as part of a long-term commitment to research in that ecosystem. Data from the study sites are digitized for export to global data repositories such as GBIF and the International Nucleotide Sequence Database Collaboration (INSDC). Ocean Sampling Day (OSD) is an initial project of the Genomic Observatories Network that involves a simultaneous sampling campaign of the world's oceans on the summer solstice of 2014 (Figure  4 ).
The broader EU FP7 Project MicroB3 is developing metadata collection protocols and workflows for OSD  [61]. Samples will be characterized for their planktonic and microbial composition as well as their water quality (e. g. , optical qualities, dissolved minerals). OSD aims to have standardized metadata that will describe the sampling process, post-capture processing of samples, data generation and analysis, and information on the sampling sites. Because OSD is a global project, it relies heavily on a distributed network of sampling stations (i. e. Genomic Observatories) spanning many countries and institutions, some in extreme environments. Consequently, the ability to relate samples and sampling processes -from the field and the lab -to analyses and publications is a major challenge for project management.
The Genomic Observatories Network established the Biocode Commons as an open, collaborative community for building the necessary informatics stack for biodiversity genomics research, such as that of OSD. Figure  4B  describes the inputs and outputs from OSD, including instances of: BCO:material sample to track physical samples such as sea water vials, filter discs, and DNA molecules; BCO:material sampling process to track events related to these samples; and BCO:identification process to track events that lead to taxon name assignments. Examples of the types of queries that could be performed in the context of the OSD use case include: 1. For a given taxon identification, show process metadata related to the relevant ocean-water filtration and collecting event. 2. Return a map of all locations where a given taxon was found on Ocean Sampling Day.
3. Show a list of identified taxa that are found in a given range of environmental conditions. 4. Discover metadata related to the DNA extraction process for a given sequence. Genetic analysis requires the expense of physical sampling, so consolidating efforts across Genomic Observatories helps to maximize the knowledge gained from these field collections by focusing efforts at scientifically important sites  [9]. Realizing the full potential of such an approach, however, requires the linking of data through ontologies, not only within projects like OSD but also between projects and across different scientific fields. Efforts to annotate biodiversity data sets with ontology terms are underway and will be available through the BCO code repository in the future.
As the BCO grows to encompass ecological survey and inventory data, it will provide a key piece of the infrastructure needed to integrate survey data more effectively via shared, linked, and well-understood terms. The use of ontology terms and globally unique identifiers, as part of a linked data framework, provides the means to answer key questions not only within complex multi-institutional projects, such as the Moorea Biocode Project and OSD, or across largescale e-infrastructure initiatives such as the Genomic Observatories Network, but also within and among single-investigator led research projects or across citizen science initiatives. For example, a query such as ''find all metagenomes collected from insects found in soil'' requires data from many sources to be linked and freely available.
This includes IAO:data item, which covers information generated as a result of an assay, such as a DNA sequence as found in Genbank. Descriptions of BCO:material samples from organismal parts required to supplement ENVO can be taken from taxon-specific anatomy ontologies as needed. As development of the BCO progressed, it became clear that the notions of material sample, subsample, and measurement were already covered in the Ontology for Biomedical Investigations (OBI). However, given its much broader scope (OBI is an ontology that aims to enable the description of all biological and clinical investigations through a shared vocabulary  [54] ), OBI contains many unfamiliar term names and extraneous classes. A first step toward alignment among the BCO, OBI, DwC, and MIxS was the proposal by BCO developers to replace BCO:material sample in future versions with OBI:specimen and simultaneously propose its adoption by both DwC and MIxS.
Conclusions The development of ontologies for biodiversity sciences aims to overcome several shortcomings of the current state of affairs: 1) a lack of clarity in the definitions of terms currently used for biodiversity data, 2) the inability to reason over complex data sets due to a lack of well-structured logical definitions, and 3) an inability to integrate museum collection data with other large biological data sets such as the GO database, environmental or metagenomic data, and survey-based data. However, these efforts can only realize their full potential when data are both digitized and shared. The development of ontologies must therefore go hand in hand with the ongoing digitization of biological collections and the development of online, sustainable data repositories that maintain stable, globally unique identifiers for data objects.
Also critically important is the development of accessible tools to enable scientists to annotate their data accurately with terms drawn from ontologies and query their data using semantically enhanced techniques. These tools ideally will be integrated with the major data repositories supporting biodiversity investigations. By providing a single unified structure for biodiversity knowledge -as opposed to ad hoc solutions that must be customized for each data set -the BCO and related ontologies permit potentially unlimited queries across data sets. This does not prohibit the construction of application-specific databases, but instead suggests that those databases should use ontology terms and URIs to make their data discoverable and interoperable; rather than replace existing vocabularies like DwC, the BCO supplements them.
Acknowledgments We thank  Chris Mungall ,  Cynthia Parr ,  Robert Hoehndorf , and two anonymous reviewers for comments on earlier versions of this manuscript,  Melissa Haendel ,  Jim Balhoff , and  Matt Yoder  for comments on the BCO,  Bjoern Peters  and the  OBI Consortium  (?http://obi-ontology. org/ page/ Consortium ) for contributing text on the  Ontology for Biomedical Ontologies , and the participants of the  Semantics of Biodiversity Workshop in Lawrence, Kansas (May, 2012 ) and the  Biocode Commons Ontology Hackathon in Oxford, England  (September, 2012) who are not co-authors of this manuscript but contributed to ontology development:  Vijay Barve ,  Jim Beach ,  Matthiew Bietz ,  Gabi Droege ,  Michelle Koo ,  John Kunze ,  Chuck Miller ,  Gil Nelson ,  Cynthia Parr ,  Sujeevan Ratnasingham ,  Jai Rideout ,  Robert Robbins ,  Phillipe Rocca-Serra ,  Joel Sachs ,  Inigo San Gil ,  Herbert Schentz ,  Peter Sterk ,  Steve Stones-Havas ,  Mellisa Tulig ,  Dave Vieglais ,  Brian Wee ,  Trish Whetzel , and  Greg Whitbread.
PLB is supported by the  European Commission  under Grant Agreement nu 287589 (MicroB3). NM is supported by the  European Commission 7th Framework Programme  ( FP7) as part of its e-Infrastructures activity  (Grant no. 283359 , BioVeL). Any opinions, findings, conclusions, or recommendations expressed in this report are those of the participants and do not necessarily represent the official views, opinions, or policy of the  National Science Foundation. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Total targets 481 A PCR amplification of the captured target libraries was performed following the manufacturer's instructions and, after its purification with the "AMPure XP beads" (BECKMAN CULTER Inc), the validation and quantification of the enriched target DNA in each library was performed using the 2100 Bioanalyzer system with the High Sensitivity DNA Kit and the 2100 Expert Software (Agilent Technologies Inc, CA, USA). The last step of the protocol was to pool samples for multiplexed sequencing in the Illumina sequencing platform MiSeq System (Illumina,Inc).
Variant analysis Data was analyzed using the platform provided by DNAnexus (www. dnanexus. com). Two different versions were used: DNAnexus Classic and the DNAnexus version recently implemented. Annotated variants were selected according to the following criteria: the quality value should be ≥250, a percentage of heterozygosity ≥30% of the reads, their annotation in the dbSNP (http://www. ncbi. nlm. nih. gov/SNP) and their description in the Usher syndrome mutation database (https://grenada. lumc. nl/ LOVD2/Usher_montpellier). Variants should be observed in both direct and reverse strands. Variants selected and suspected to be pathogenic were confirmed by Sanger sequencing. The DNA fragments containing the variants were amplified by PCR with specific primers and were sequenced on both strands using the Big Dye 3.
Copy number variation analysis and validation The coverage of every target region of the sample of interest was normalized and compared with average normalized data of all other samples of the same run to obtain the ratio relative coverage. Deletions and duplications were suspected of being present if this ratio fell below 0. 7 or rose above 1. 3 respectively. Validation of rearrangements of USH2A and PCDH15 was performed by MLPA analysis. For USH2A the SALSA MLPA probemixes P361 and P362 were used. To confirm the PCDH15 copy number variations the SALSA MLPA probemix P292 was employed. The MLPA reactions were performed according to the manufacturer's recommendations (http://www. mlpa. com). In the cases of putative rearrangements identified in CDH23 and GPR98, their validation was carried out by an oligonucleotide array-CGH.
These variants included 21 missense, 8 nonsense, 9 frameshifts, 9 intronic mutations and 6 large rearrangements. The DNA nexus program showed a high number of novel variants in each patient. These changes were selected according to their nature, or the presence of another pathogenic allele in the same gene in the same patient. The variants were confirmed by Sanger sequencing and missense, isocoding or intronic variants were analyzed by different in silico algorithms to predict their pathogenic effect. According to the bioinformatics predictions, 15 variants out of 25 studied variants were considered as pathogenic (UV4) or possibly pathogenic (UV3) (Table  4 ). Segregation analysis of detected alleles with the respective family members was performed in 13 cases, and cosegregation of the mutations with the disease was verified in all these cases (Table  3 ).
05, and tolerated if the score is > 0. 05. PolyPhen stablish three classifications: "Probably damaging" (it is believed most likely to affect protein function or structure), "Possibly damaging" (it is believed to affect protein function or structure), "Benign" (most likely lacking any phenotypic effect). ESE: Exonic Splicing Enhancer. Large rearrangements in USH2A and PCDH15 were subsequently confirmed by MLPA, whereas patients that carried duplications affecting CDH23 and GPR98 were analyzed by a-CGH (data not shown). In 5 patients, the rearrangements detected by comparison of normalized coverage data were confirmed. The a-CGH technique to confirm the presence of the heterozygous duplication of CDH23 (exon 29) in patient RP-1781 was not succesful due to technical problems. Unfortunately, it was impossible to get a new DNA sample for this patient to repeat the experiment.
Acknowledgements We are grateful to FARPE and all the patients involved in this study. This work was supported by grant  PI13/00638  and  PI13/00226  from  Fondo de Investigaciones Sanitarias (FIS)  from the  Spanish Government. We are grateful to ONCE,  Retina CV  and  FUNDALUCE. CIBERER (CB/06/07/1030 ) is an initiative of the  Institute of Health Carlos III  from the  Spanish Government. The bioanalyzer ABI3500xl was purchased with the grant  PROMIS (II12/00023 ) from the  Instituto de Salud Carlos III. MJA is recipient of a fellowship from the  Ministerio de Educación  (REF:  AP2009-3344 ). RR and RPVM are Miguel Servet fellows ( CP09/0118  and  CP11/00090 , respectively) from the  Institute of Health Carlos III , which is partially supported by the  European Regional Development Fund.
Studying structure on the last scattering surface is the best-known way to probe the global organisation of our Universe and the CMB provides the most detailed and best understood dataset for this purpose. We can also relax assumptions about the global structure of spacetime by allowing anisotropy about each point in the Universe. This yields more general solutions to Einstein's field equations, leading to the so-called Bianchi cosmologies. For small anisotropy, as demanded by current observations, linear perturbation about the standard FRW model may be applied. A universal shear and rotation induce a characteristic subdominant, deterministic signature in the CMB, which is embedded in the usual stochastic anisotropies. The deterministic CMB temperature fluctuations that result in the homogenous Bianchi models were first examined by  Collins & Hawking (1973)  and  Barrow et al.