Related papers: Scientific data searching, sharing and retrieval
Machine Science, or Data-driven Research, is a new and interesting scientific methodology that uses advanced computational techniques to identify, retrieve, classify and analyse data in order to generate hypotheses and develop models. In…
We analyse the issues involved in the management and mining of astrophysical data. The traditional approach to data management in the astrophysical field is not able to keep up with the increasing size of the data gathered by modern…
Open data is an emerging paradigm to share large and diverse datasets -- primarily from governmental agencies, but also from other organizations -- with the goal to enable the exploitation of the data for societal, academic, and commercial…
Research papers in the biomedical field come with large and complex data sets that are shared with the scientific community as unstructured data files via public data repositories. Examples are sequencing, microarray, and mass spectroscopy…
Materials science is becoming increasingly more reliant on digital data to facilitate progress in the field. Due to a large diversity in its scope, breadth, and depth, organizing the data in a standard way to optimize the speed and creative…
Without sufficient information about research data practices occurring in a particular research organisation, there is a risk of mismatching research data service efforts with the needs of its researchers. This study describes how data…
Scientific endeavors such as large astronomical surveys generate databases on the terabyte scale. These, usually multidimensional databases must be visualized and mined in order to find interesting objects or to extract meaningful and…
Most domains of science are experiencing a paradigm shift due to the advent of a new generation of instruments and detectors which produce data and data streams at an unprecedented rate. The scientific exploitation of these data, namely…
The extraction of information form high-throughput experiments is a key aspect of modern biology. Early in the development of microarray technology, researchers recognized that the size of the datasets and the limitations of both…
For more than a century, scientists have been collecting behavioral data--an increasing fraction of which is now being publicly shared so other researchers can reuse them to replicate, integrate or extend past results. Although behavioral…
Over the past 40 years, database management systems (DBMSs) have evolved to provide a sophisticated variety of data management capabilities. At the same time, tools for managing queries over the data have remained relatively primitive. One…
Digital libraries for research, such as the ACM Digital Library or Semantic Scholar, do not enable the machine-supported, efficient reuse of scientific knowledge (e.g., in synthesis research). This is because these libraries are based on…
For more than a decade there has been a push in the planetary science community to support interoperable methods for accessing and working with geospatial data. Common geospatial data products for planetary research include image mosaics,…
The SmartSHARK repository mining data is a collection of rich and detailed information about the evolution of software projects. The data is unique in its diversity and contains detailed information about each change, issue tracking data,…
The rapid expansion of Earth Science data from satellite observations, reanalysis products, and numerical simulations has created a critical bottleneck in scientific discovery, namely identifying relevant datasets for a given research…
Collaborations in astronomy and astrophysics are faced with numerous cyber infrastructure challenges, such as large data sets, the need to combine heterogeneous data sets, and the challenge to effectively collaborate on those large,…
Within the past few decades we have witnessed digital revolution, which moved scholarly communication to electronic media and also resulted in a substantial increase in its volume. Nowadays keeping track with the latest scientific…
In materials sciences, a large amount of research data is generated through a broad spectrum of different experiments. As of today, experimental research data including meta-data in materials science is often stored decentralized by the…
Unsupervised machine learning is widely used to mine large, unlabeled datasets to make data-driven discoveries in critical domains such as climate science, biomedicine, astronomy, chemistry, and more. However, despite its widespread…
A distributed data warehouse system is one of the actual issues in the field of astroparticle physics. Famous experiments, such as TAIGA, KASCADE-Grande, produce tens of terabytes of data measured by their instruments. It is critical to…