To summarize the survey's findings: Curation of digital data is a concern for a significant proportion of UCSB faculty and researchers. Curation of digital data is a concern for almost every department and unit on campus. Researchers almost universally view themselves as personally responsible for the curation of their data. Researchers view curation as a collaborative activity and collective responsibility. Departments have different curation requirements, and therefore may require different amounts and types of campus support. Researchers desire help with all data management activities related to curation, predominantly storage. Researchers may be underestimating the need for help using archival storage systems and dealing with attendant metadata issues. There are many sources of curation mandates, and researchers are increasingly under mandate to curate their data. Researchers under curation mandate are more likely to collaborate with other parties in curating their data, including with their local labs and departments. Researchers under curation mandate request more help with all curation-related activities; put another way, curation mandates are an effective means of raising curation awareness. The survey reflects the concerns of a broad cross-section of campus.

In 2012 the Data Curation @ UCSB Project surveyed UCSB campus faculty and researchers on the subject of data curation, with the goals of 1) better understanding the scope of the digital curation problem and the curation services that are needed, and 2) characterizing the role that the UCSB Library might play in supporting curation of campus research outputs.

The project will develop an analysis package in the open-source language R and complement it with a step-by-step hands-on manual to make tools available to a broad, international user community that includes academics, scientists working for governments and non-governmental organizations, and professionals directly engaged in conservation practice and land management. The software package will be made publicly available under http://www.clfs.umd.edu/biology/faganlab/movement/.

These species represent not only different types of movement (on land, in air, in water) but also different types of relocation data (from visual observations of individually marked animals to GPS relocations to relocations obtained from networked sensor arrays).

Once a searchable atlas has been constructed there are fundamentally two approaches that can be used to analyze the data: one visual, the other mathematical.

The initial inputs for deriving quantitative information of gene expression and embryonic morphology are raw image data, either of fluorescent proteins expressed in live embryos or of stained fluorescent markers in fixed material. These raw images are then analyzed by computational algorithms that extract features, such as cell location, cell shape, and gene product concentration. Ideally, the extracted features are then recorded in a searchable database, an atlas, that researchers from many groups can access. Building a database with quantitative graphical and visualization tools has the advantage of allowing developmental biologists who lack specialized skills in imaging and image analysis to use their knowledge to interrogate and explore the information it contains.

We regularly provide scholars with access to content for this purpose. Our Data for Research site (http://dfr.jstor.org)

Not even gephi is very good at visualising temporal networks.