Alvania guancha Moolenbeek et Hoensbelaar 1989

Catálogo do Museo de Historia Natural USC. n. inventario 10001

Manzonia heroensis Moolenbeek et Hoenselaar, 1992

Catálogo do Museo de Historia Natural USC. n. inventario 10030

DataJoint: managing big scientific data using MATLAB or Python

The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and processed in a variety of ways to extract new insights. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically dispersed, and rarely possess a primary interest in data science. Here we describe DataJoint, an open-source toolbox designed for manipulating and processing scientific data under the relational data model. Designed for scientists who need a flexible and expressive database language with few basic concepts and operations, DataJoint facilitates multi-user access, efficient queries, and distributed computing. With implementations in both MATLAB and Python, DataJoint is not limited to particular file formats, acquisition systems, or data modalities and can be quickly adapted to new experimental designs. DataJoint and related resources are available at http://datajoint.github.com

DataJoint: managing big scientific data using MATLAB or Python

The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and processed in a variety of ways to extract new insights. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically dispersed, and rarely possess a primary interest in data science. Here we describe DataJoint, an open-source toolbox designed for manipulating and processing scientific data under the relational data model. Designed for scientists who need a flexible and expressive database language with few basic concepts and operations, DataJoint facilitates multi-user access, efficient queries, and distributed computing. With implementations in both MATLAB and Python, DataJoint is not limited to particular file formats, acquisition systems, or data modalities and can be quickly adapted to new experimental designs. DataJoint and related resources are available at http://datajoint.github.com

Alvania piersmai Moolenbeek R. et Hoenselaar H.J., 1989

Catálogo do Museo de Historia Natural USC. n. inventario 10001

DataJoint: Managing Big Scientific Data Using Matlab or Python

The rise of big data in modern research poses serious challenges for data management: Large and intricate datasets from diverse instrumentation must be precisely aligned, annotated, and organized in a flexible way that allows swift exploration and analysis. Data management should guarantee consistency of intermediate results in subsequent multi-step processing pipelines such that changes in one part automatically propagate to all downstream results. Finally, data organization should be self-documenting to ensure that lab members and collaborators can access the data with minimal effort, even years after it was collected. While high levels of data integrity are expected, research teams have diverse backgrounds, are geographically dispersed, and rarely possess a primary interest in data science. While the challenges associated with large, complex data sets may be new to biologists, they have been addressed quite successfully in other contexts by relational databases, which provide a principled approach for effective data management. DataJoint is an open-source framework that provides a clean implementation of core concepts of the relational data model to facilitate multi-user access, effcient queries, distributed computing, and cascading dependencies across multiple data modalities. Critically, while DataJoint relies on an established relational database management system (MySQL) as its backend, data access and manipulation are performed transparently in the commonly-used languages MATLAB or Python, and DataJoint can be integrated into new and existing analyses written in these languages with minimal effort or additional training. DataJoint is not limited to particular file formats, acquisition systems, or data modalities and can be quickly adapted to new experimental designs. DataJoint and related resources are available at http://datajoint.github.com

Saturated Fat and Cardiovascular Disease: A Review of Current Evidence

Cardiovascular disease (CVD) is one of the leading causes of death throughout the world. Saturated fatty acids (SFA) have long been implicated in the development of CVD. The evidence to support this hypothesis came from studies which examined the effects of SFA intake on total cholesterol (TC). However, relying on TC as the sole primary outcome may not be sufficient and understanding the effect of SFA on the concentrations of other lipid fractions is necessary. SFA are known to increase low-density lipoprotein cholesterol (LDL-C) and consequently dietary guidelines recommend reducing SFA intakes in order to decrease LDL-C and CVD risk. However, recent evidence suggests that not all SFAs possess the same atherogenic properties but this development has not yet been reflected in dietary recommendations. This review summarizes recent evidence on the relationship between SFA intake and CVD risk. It also explores current dietary guidelines specific to SFA intake and outlines why future guidelines may need to be food- rather than nutrient-specific. Overall, the evidence presented in this review suggests that not all SFA are created equal and the food sources of SFA, as well as individual characteristics of the SFA, such as chain length, should be considered in dietary recommendations