During the last years, considerable progresses have been made in developing on-line species occurrence databases. These are crucial in scientific activities on biodiversity, including the generation of species distribution models, which play an important role in conservation efforts. Unfortunately, their exploitation is still difficult and time consuming for many scientists. No database currently exists that can claim to host, and make available in a seamless way, all the species occurrence data needed by the ecology scientific community. Occurrence data are scattered among several databases and information systems. It is not easy to retrieve records from them, because of differences in the adopted protocols, formats and granularity. Once collected, datasets have to be selected, homogenized and pre-processed before being ready-to-use in scientific analysis and modeling. This paper introduces a set of facilities offered by the D4Science Data Infrastructure to support these phases of the scientific process. It also exemplifies how they contribute to reduce the time spent in data quality assessment and curation thus improving the overall performance of the scientific investigation.

D4Science facilities for managing biodiversity databases

Candela L;Castelli D;Coro G;De Faveri F;Lelii L;Mangiacrapa F;Marioli V;Pagano P
2013

Abstract

During the last years, considerable progresses have been made in developing on-line species occurrence databases. These are crucial in scientific activities on biodiversity, including the generation of species distribution models, which play an important role in conservation efforts. Unfortunately, their exploitation is still difficult and time consuming for many scientists. No database currently exists that can claim to host, and make available in a seamless way, all the species occurrence data needed by the ecology scientific community. Occurrence data are scattered among several databases and information systems. It is not easy to retrieve records from them, because of differences in the adopted protocols, formats and granularity. Once collected, datasets have to be selected, homogenized and pre-processed before being ready-to-use in scientific analysis and modeling. This paper introduces a set of facilities offered by the D4Science Data Infrastructure to support these phases of the scientific process. It also exemplifies how they contribute to reduce the time spent in data quality assessment and curation thus improving the overall performance of the scientific investigation.
2013
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Data integration
Data sharing
Digital Libraries
Data processing
Hybrid Data Infrastructure
Virtual Research Environment
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/262990
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