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

Temperature drives variation in flying insect biomass across a German malaise trap network

1. Among the many concerns for biodiversity in the Anthropocene, recent reports of flying insect loss are particularly alarming, given their importance as pollinators, pest control agents, and as a food source. Few insect monitoring programmes cover the large spatial scales required to provide more generalizable estimates of insect responses to global change drivers. 2. We ask how climate and surrounding habitat affect flying insect biomass using data from the first year of a new monitoring network at 84 locations across Germany comprising a spatial gradient of land cover types from protected to urban and crop areas. 3. Flying insect biomass increased linearly with temperature across Germany. However, the effect of temperature on flying insect biomass flipped to negative in the hot months of June and July when local temperatures most exceeded long-term averages. 4. Land cover explained little variation in insect biomass, but biomass was lowest in forests. Grasslands, pastures, and orchards harboured the highest insect biomass. The date of peak biomass was primarily driven by surrounding land cover, with grasslands especially having earlier insect biomass phenologies. 5. Standardised, large-scale monitoring provides key insights into the underlying processes of insect decline and is pivotal for the development of climate-adapted strategies to promote insect diversity. In a temperate climate region, we find that the positive effects of temperature on flying insect biomass diminish in a German summer at locations where temperatures most exceeded long-term averages. Our results highlight the importance of local adaptation in climate change-driven impacts on insect communities