@conference {1414,
	title = {A Computational- and Storage-Cloud for Integration of Biodiversity Collections},
	booktitle = {Proceedings of the 2013 IEEE 9th International Conference on e-Science},
	year = {2013},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Beijing, China},
	abstract = {A core mission of the Integrated Digitized Biocollections (iDigBio) project is the building and deployment of a cloud computing environment customized to support the digitization workflow and integration of data from all U.S. nonfederal biocollections. iDigBio chose to use cloud computing technologies to deliver a cyberinfrastructure that is flexible, agile, resilient, and scalable to meet the needs of the biodiversity community. In this context, this paper describes the integration of open source cloud middleware, applications, and third party services using standard formats, protocols, and services. In addition, this paper demonstrates the value of the digitized information from collections in a broader scenario involving multiple disciplines.},
	keywords = {appliances, biocollections infrastructure, biodiversity, cloud computing, data access, data store, digitization, EF-1115210},
	doi = {10.1109/eScience.2013.48},
	url = {http://dx.doi.org/10.1109/eScience.2013.48},
	author = {Matsunaga, Andr{\'e}a and Thompson, Alex and Figueiredo, Renato J. and Germain-Aubrey, Charlotte C. and Collins, Matthew and Beaman, Reed S. and MacFadden, Bruce J. and Riccardi, Greg and Soltis, Pamela S. and Page, Lawrence M. and Fortes, Jos{\'e} A.B.}
}
@article {1904,
	title = {Digitization Tools Optical Character Recognition, Parsing, Consensus in Crowdsourcing },
	year = {2014},
	keywords = {SiBBr 2014},
	url = {https://www.idigbio.org/wiki/images/4/49/MatsunagaToolsSibbr2014-15-50.pdf},
	author = {Matsunaga, Andr{\'e}a}
}
@conference {2439,
	title = {Lifemapper{\textquoteright}s species distribution modeling based on iDigBio Biocollections},
	booktitle = {SPNHC 2015},
	year = {2015},
	keywords = {EF-1115210, Presentation, SPNHC2015},
	author = {Stewart, Aimee and Matsunaga, Andr{\'e}a and Beach, James H. and Grady, C.J. and Cavner, Jeffrey and Fortes, Jos{\'e} A.B.}
}
@conference {2110,
	title = {Mapping Specifications for Ranked Hierarchical Trees in Data Integration Systems},
	booktitle = {2014 IEEE International Conference on Information Reuse and Integration (IRI 2014)},
	year = {2014},
	publisher = {IEEE},
	organization = {IEEE},
	address = {San Francisco, California, USA},
	abstract = {A popular approach to deal with data integration of heterogeneous data sources is to Extract, Transform and Load (ETL) data from disparate sources into a consolidated data store while addressing integration challenges including, but not limited to: structural differences in the source and target schemas, semantic differences in their vocabularies, and data encoding. This work focuses on the integration of tree-like hierarchical data or information that when modeled as a relational schema can take the shape of a flat schema, a self-referential schema or a hybrid schema. Examples include evolutionary taxonomies, geological time scales, and organizational charts. Given the observed complexity in developing ETL processes for this particular but common type of data, our work focuses on reducing the time and effort required to map and transform this data. Our research automates and simplifies the transformation from ranked self-referential to flat representations (and vice-versa), by: (a) proposing MSL+, an extension to IBM{\textquoteright}s Mapping Specification Language (MSL), to succinctly express the mapping between schemas while hiding the actual transformation implementation complexity from the user, and (b) implementing a transformation component for the Talend open-source ETL platform, called Tree Transformer (TT). We evaluated MSL+ and TT, in the context of biodiversity data integration, where this class of transformations is a recurring pattern. We demonstrate the effectiveness of MSL+ with respect to development time savings as well as a 2 to 25-fold performance improvement in transformation time achieved by TT when compared to existing implementations and to Talend built-in components.},
	keywords = {Data Integration, data transformation, EF-1115210, ETL, mapping language, self-referential schema},
	doi = {10.1109/IRI.2014.7051899},
	url = {http://ieeexplore.ieee.org/xpls/icp.jsp?arnumber=7051899},
	author = {Soomro, Sarfaraz and Matsunaga, Andr{\'e}a and Fortes, Jos{\'e} A.B.}
}
@article {2138,
	title = {Meeting report: Identifying practical applications of ontologies for biodiversity informatics},
	journal = {Standards in Genomic Sciences C7 - 25},
	volume = {10},
	year = {2015},
	pages = {1-6},
	abstract = {This report describes the outcomes of a recent workshop, building on a series of workshops from the last three years with the goal if integrating genomics and biodiversity research, with a more specific goal here to express terms in Darwin Core and Audubon Core, where class constructs have been historically underspecified, into a Biological Collections Ontology (BCO) framework. For the purposes of this workshop, the BCO provided the context for fully defining classes as well as object and data properties, including domain and range information, for both the Darwin Core and Audubon Core. In addition, the workshop participants reviewed technical specifications and approaches for annotating instance data with BCO terms. Finally, we laid out proposed activities for the next 3 to 18 months to continue this work.},
	keywords = {Ontology Biodiversity Population Community Darwin core OWL RDF Microbial ecology Sequencing},
	doi = {10.1186/s40793-015-0014-0},
	url = {http://dx.doi.org/10.1186/s40793-015-0014-0},
	author = {Deck, John and Guralnick, Robert and Walls, Ramona and Blum, Stanley and Haendel, Melissa and Matsunaga, Andr{\'e}a and Wieczorek, John}
}
@article {1780,
	title = {Reaching Consensus in Crowdsourced Transcription of Biocollections Information},
	year = {2014},
	abstract = {Crowdsourcing can be a cost-effective method for tackling the problem of digitizing historical biocollections data, and a number of crowdsourcing platforms have been developed to facilitate interaction with the public and to design simple {\textquotedblleft}Human Intelligence Tasks{\textquotedblright}. However, the problem of reaching consensus on the response of the crowd is still challenging for tasks for which a simple majority vote is inadequate. This paper (a) describes the challenges faced when trying to reach consensus on data transcribed by different workers, (b) offers consensus algorithms for textual data and a consensus-based controller to assign a dynamic number of workers per task, and (c) proposes further enhancements of future crowdsourcing tasks in order to minimize the need for complex consensus algorithms. Experiments using the proposed algorithms show up to a 45-fold increase in ability to reach consensus when compared to majority voting using exact string matching. In addition, the controller is able to decrease the crowdsourcing cost by 55\% when compared to a strategy that uses a fixed number of workers.},
	keywords = {Citizen Science, crowdsourcing},
	url = {https://www.idigbio.org/sites/default/files/internal-docs/presentations/matsunagacrowdsourcingescience2014-141106090530-conversion-gate01.pdf},
	author = {Matsunaga, Andr{\'e}a and Austin Mast and Fortes, Jos{\'e} A.B.}
}
@conference {1824,
	title = {Reaching Consensus in Crowdsourced Transcription of Biocollections Information},
	booktitle = {2014 IEEE 10th International Conference on e-Science},
	year = {2014},
	month = {Oct/2014},
	publisher = {IEEE},
	organization = {IEEE},
	address = {Guaruj{\'a}, Brazil},
	abstract = {Crowdsourcing can be a cost-effective method for tackling the problem of digitizing historical bio collections data, and a number of crowd sourcing platforms have been developed to facilitate interaction with the public and to design simple "Human Intelligence Tasks". However, the problem of reaching consensus on the response of the crowd is still challenging for tasks for which a simple majority vote is inadequate. This paper (a) describes the challenges faced when trying to reach consensus on data transcribed by different workers, (b) offers consensus algorithms for textual data and a consensus-based controller to assign a dynamic number of workers per task, and (c) proposes further enhancements of future crowd sourcing tasks in order to minimize the need for complex consensus algorithms. Experiments using the proposed algorithms show up to a 45-fold increase in ability to reach consensus when compared to majority voting using exact string matching. In addition, the controller is able to decrease the crowd sourcing cost by 55\% when compared to a strategy that uses a fixed number of workers.},
	keywords = {Accuracy, Approximation algorithms, biocollections infrastructure, biodiversity, consensus algorithms, crowdsourcing, digitization, EF-1115210, Heuristic algorithms, Materials, Optical character recognition software, Reliability},
	doi = {10.1109/eScience.2014.30},
	url = {http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=\&arnumber=6972249},
	author = {Matsunaga, Andr{\'e}a and Mast, Austin and Fortes, Jos{\'e} A.B.}
}
@article {1623,
	title = {Semantics in Support of Biodiversity Knowledge Discovery: An Introduction to the Biological Collections Ontology and Related Ontologies.},
	journal = {PLoS ONE},
	volume = {9},
	year = {2014},
	month = {03/2014},
	chapter = {1},
	abstract = {The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.},
	keywords = {EF-1115210},
	url = {http://www.plosone.org/article/info\%3Adoi\%2F10.1371\%2Fjournal.pone.0089606},
	author = {Ramona Wells and John Deck and Robert Guralnick and Steve Baskauf and Reed Beaman and Stanley Blum and Shawn Bowers and Pier Luigi Buttigieg and Neil Davies and Dag Endresen and Maria Alejandra Gandolfo and Robert Hanner and Alyssa Janning and Leonard Krishtalka and Matsunaga, Andr{\'e}a and Peter Midford and Norman Morrison and Eamonn O. Tuama and Mark Schidhauer and Barry Smith and Brian J. Stucky and Andrea Thomer and John Wieczorek and Jamie Whitacre and John Wooley}
}
@inbook {2111,
	title = {Simplifying Extract-Transform-Load for Ranked Hierarchical Trees via Mapping Specifications},
	booktitle = {Formalisms for Reuse and Systems Integration},
	series = {Advances in Intelligent Systems and Computing},
	volume = {346},
	year = {2015},
	pages = {203-225},
	publisher = {Springer International Publishing},
	organization = {Springer International Publishing},
	abstract = {A popular approach to deal with data integration of heterogeneous data sources is to Extract, Transform and Load (ETL) data from disparate sources into a consolidated data store while addressing integration challenges including, but not limited to: structural differences in the source and target schemas, semantic differences in their vocabularies, and data encoding. This work focuses on the integration of tree-like hierarchical data or information that when modeled as a relational schema can take the shape of a flat schema, a self-referential schema or a hybrid schema. Examples include evolutionary taxonomies, geological time scales, and organizational charts. Given the observed complexity in developing ETL processes for this particular but common type of data, our work focuses on reducing the time and effort required to map and transform this data. Our research automates and simplifies all possible transformations involving ranked self-referential and flat representations, by: (a) proposing MSL+, an extension to IBM{\textquoteright}s Mapping Specification Language (MSL), to succinctly express the mapping between schemas while hiding the actual transformation implementation complexity from the user, and (b) implementing a transformation component for the Talend open-source ETL platform, called Tree Transformer (TT). We evaluated MSL+ and TT, in the context of biodiversity data integration, where this class of transformations is a recurring pattern. We demonstrate the effectiveness of MSL+ with respect to development time savings as well as a 2 to 25-fold performance improvement in transformation time achieved by TT when compared to existing implementations and to Talend built-in components.},
	keywords = {Data Integration, data transformation, EF-1115210, ETL, flat schema, hierarchical tree, mapping language, MSL, schema mapping, self-referential schema},
	issn = {978-3-319-16576-9},
	doi = {10.1007/978-3-319-16577-6_9},
	url = {http://dx.doi.org/10.1007/978-3-319-16577-6_9},
	author = {Soomro, Sarfaraz and Matsunaga, Andr{\'e}a and Fortes, Jos{\'e} A.B.},
	editor = {Bouabana-Tebibel, Thouraya and Rubin, Stuart H.}
}
@conference {2139,
	title = {A Specimen-based View of the World: Using the Biological Collections Ontology to Model Biodiversity Collections},
	booktitle = {2014 ICBO Workshop: Starting an OBI-based Biobank Ontology},
	year = {2014},
	address = {Houston, TX, USA},
	url = {http://ceur-ws.org/Vol-1309/paper8.pdf},
	author = {Walls, Ramona and Guralnick, Robert and Deck, John and Matsunaga, Andr{\'e}a},
	editor = {Brochhausen, Mathias and Haendel, Melissa and Stoeckert, Christian J. and Zheng, Jie}
}
@article {2291,
	title = {Workforce-efficient consensus in crowdsourced transcription of biocollections information},
	journal = {Future Generation Computer Systems},
	year = {2015},
	abstract = {Abstract Crowdsourcing can be a cost-effective method for tackling the problem of digitizing historical biocollections data, and a number of crowdsourcing platforms have been developed to facilitate interaction with the public and to design simple {\^a}{\texteuro}{\oe}Human Intelligence Tasks{\^a}{\texteuro}. However, the problem of reaching consensus on the response of the crowd is still challenging for tasks for which a simple majority vote is inadequate. This paper (a) describes the challenges faced when trying to reach consensus on data transcribed by different workers, (b) offers consensus algorithms for textual data, (c) implements a consensus-based controller to assign a dynamic number of workers per task and per field of a task, (d) proposes the use of clustering to further eliminate redundant work and (e) proposes enhancements of future crowdsourcing task assignments in order to minimize the need for complex consensus algorithms. Experiments using the proposed algorithms show multifold increase in the ability to reach consensus when compared to majority voting using exact string matching. In addition, the workforce controller is able to decrease the crowdsourcing cost per task and per task field by 37\% and 50\%, respectively, when compared to a strategy that uses a fixed number of workers. The accuracy of clustering is also good and it has the potential to increase the quality of tasks that can be clustered.},
	keywords = {biocollections infrastructure, biodiversity, consensus algorithms, crowdsourcing, digitization, EF-1115210},
	doi = {http://dx.doi.org/10.1016/j.future.2015.07.004},
	url = {http://www.sciencedirect.com/science/article/pii/S0167739X15002277},
	author = {Matsunaga, Andr{\'e}a and Mast, Austin and Fortes, Jos{\'e} A. B.}
}
