The increasing data volume and highly complex models used in different domains make it difficult to debug models in cases of anomalies. Data provenance provides scientists sufficient information to investigate their models. In this paper, we propose a tool which can infer fine-grained data provenance based on a given script. The tool is demonstrated using a hydrological model. The tool is also tested success-fully handling other scripts in different contexts
The article of record as published may be found at http://dx.doi.org/10.1098/rsta.2008.0187Computati...
AbstractReproducibility of scientific research relies on accurate and precise citation of data and t...
Data management is growing in complexity as large-scale applications take advantage of the loosely c...
Scientists require provenance information either to validate their model or to investigate the origi...
The web, and more recently the concept and technology of the Semantic Web, has created a wealth of n...
Abstract. We propose noWorkflow, a tool that transparently captures provenance of scripts and enable...
Data provenance allows scientists to validate their model as well as to investigate the origin of an...
Data provenance is the history of a digital artifact, from the point of collection to its present<br...
Scientists can facilitate data intensive applications to study and understand the behavior of a comp...
Data provenance allows scientists in different domains validating their models and algorithms to fin...
In this paper, we propose a provenance model able to represent the provenance of any data object cap...
In many application areas like e-science and data-warehousing detailed information about the origin ...
Fine-grained data provenance ensures reproducibility of results in decision making, process control ...
Scientists and, more generally end users of computer systems, need to be able to trust the data they...
In science, results that are not reproducible by peer scientists are valueless and of no significanc...
The article of record as published may be found at http://dx.doi.org/10.1098/rsta.2008.0187Computati...
AbstractReproducibility of scientific research relies on accurate and precise citation of data and t...
Data management is growing in complexity as large-scale applications take advantage of the loosely c...
Scientists require provenance information either to validate their model or to investigate the origi...
The web, and more recently the concept and technology of the Semantic Web, has created a wealth of n...
Abstract. We propose noWorkflow, a tool that transparently captures provenance of scripts and enable...
Data provenance allows scientists to validate their model as well as to investigate the origin of an...
Data provenance is the history of a digital artifact, from the point of collection to its present<br...
Scientists can facilitate data intensive applications to study and understand the behavior of a comp...
Data provenance allows scientists in different domains validating their models and algorithms to fin...
In this paper, we propose a provenance model able to represent the provenance of any data object cap...
In many application areas like e-science and data-warehousing detailed information about the origin ...
Fine-grained data provenance ensures reproducibility of results in decision making, process control ...
Scientists and, more generally end users of computer systems, need to be able to trust the data they...
In science, results that are not reproducible by peer scientists are valueless and of no significanc...
The article of record as published may be found at http://dx.doi.org/10.1098/rsta.2008.0187Computati...
AbstractReproducibility of scientific research relies on accurate and precise citation of data and t...
Data management is growing in complexity as large-scale applications take advantage of the loosely c...