Data-rich Science Research Articles

Centering Educators’ Voices in the Development of Professional Learning for Data-Rich, Place-Based Science Instruction

This self-reflective case study describes our project team’s efforts to promote equity in science professional learning (PL) by centering the voices of educators in the PL design process and within the course itself. We believe that educators’ experiences, priorities, and expertise are essential to developing professional learning that meets the needs of teachers and their students. We have a particular interest in amplifying the voices of those in historically underrepresented communities. Toward that end, we engaged science educators who work with Indigenous students and recent immigrants as collaborators in developing PL to support data-rich, place-based Earth Science instruction. In this case study, we share and critique the practices and tools that we have employed to center educator voices, rather than those of the PL designers and researchers. Our strategies for developing more equitable science professional learning include the use of: (a) equity-focused research methods, such as asset-based needs-sensing questions and peer-to-peer interviews; (b) a humanistic stance toward data-rich science learning, which emphasizes the typically unnamed sociocultural inputs and outputs that permeate all aspects of data; (c) a participatory design process that centers educators’ voices; and (d) a model of professional learning that uses representations of educator and student experiences as objects for reflection.

Challenges and Opportunities for Bayesian Statistics in Proteomics.

Proteomics is a data-rich science with complex experimental designs and an intricate measurement process. To obtain insights from the large data sets produced, statistical methods, including machine learning, are routinely applied. For a quantity of interest, many of these approaches only produce a point estimate, such as a mean, leaving little room for more nuanced interpretations. By contrast, Bayesian statistics allows quantification of uncertainty through the use of probability distributions. These probability distributions enable scientists to ask complex questions of their proteomics data. Bayesian statistics also offers a modular framework for data analysis by making dependencies between data and parameters explicit. Hence, specifying complex hierarchies of parameter dependencies is straightforward in the Bayesian framework. This allows us to use a statistical methodology which equals, rather than neglects, the sophistication of experimental design and instrumentation present in proteomics. Here, we review Bayesian methods applied to proteomics, demonstrating their potential power, alongside the challenges posed by adopting this new statistical framework. To illustrate our review, we give a walk-through of the development of a Bayesian model for dynamic organic orthogonal phase-separation (OOPS) data.

Paradigm Shift Toward Digital Neuropsychology and High-Dimensional Neuropsychological Assessments: Review

Neuropsychologists in the digital age have increasing access to emerging technologies. The National Institutes of Health (NIH) initiatives for behavioral and social sciences have emphasized these developing scientific and technological potentials (eg, novel sensors) for augmented characterization of neurocognitive, behavioral, affective, and social processes. Perhaps these innovative technologies will lead to a paradigm shift from disintegrated and data-poor behavioral science to cohesive and data-rich science that permits improved translation from bench to bedside. The 4 main advances influencing the scientific priorities of a recent NIH Office of Behavioral and Social Sciences Research strategic plan include the following: integration of neuroscience into behavioral and social sciences, transformational advances in measurement science, digital intervention platforms, and large-scale population cohorts and data integration. This paper reviews these opportunities for novel brain-behavior characterizations. Emphasis is placed on the increasing concern of neuropsychology with these topics and the need for development in these areas to maintain relevance as a scientific discipline and advance scientific developments. Furthermore, the effects of such advancements necessitate discussion and modification of training as well as ethical and legal mandates for neuropsychological research and praxes.

Data-Rich Astronomy: Mining Sky Surveys with PhotoRApToR

AbstractIn the last decade a new generation of telescopes and sensors has allowed the production of a very large amount of data and astronomy has become a data-rich science. New automatic methods largely based on machine learning are needed to cope with such data tsunami. We present some results in the fields of photometric redshifts and galaxy classification, obtained using the MLPQNA algorithm available in the DAMEWARE (Data Mining and Web Application Resource) for the SDSS galaxies (DR9 and DR10). We present PhotoRApToR (Photometric Research Application To Redshift): a Java based desktop application capable to solve regression and classification problems and specialized for photo-z estimation.

Knowledge management for systems biology a general and visually driven framework applied to translational medicine.

BackgroundTo enhance our understanding of complex biological systems like diseases we need to put all of the available data into context and use this to detect relations, pattern and rules which allow predictive hypotheses to be defined. Life science has become a data rich science with information about the behaviour of millions of entities like genes, chemical compounds, diseases, cell types and organs, which are organised in many different databases and/or spread throughout the literature. Existing knowledge such as genotype - phenotype relations or signal transduction pathways must be semantically integrated and dynamically organised into structured networks that are connected with clinical and experimental data. Different approaches to this challenge exist but so far none has proven entirely satisfactory.ResultsTo address this challenge we previously developed a generic knowledge management framework, BioXM™, which allows the dynamic, graphic generation of domain specific knowledge representation models based on specific objects and their relations supporting annotations and ontologies. Here we demonstrate the utility of BioXM for knowledge management in systems biology as part of the EU FP6 BioBridge project on translational approaches to chronic diseases. From clinical and experimental data, text-mining results and public databases we generate a chronic obstructive pulmonary disease (COPD) knowledge base and demonstrate its use by mining specific molecular networks together with integrated clinical and experimental data.ConclusionsWe generate the first semantically integrated COPD specific public knowledge base and find that for the integration of clinical and experimental data with pre-existing knowledge the configuration based set-up enabled by BioXM reduced implementation time and effort for the knowledge base compared to similar systems implemented as classical software development projects. The knowledgebase enables the retrieval of sub-networks including protein-protein interaction, pathway, gene - disease and gene - compound data which are used for subsequent data analysis, modelling and simulation. Pre-structured queries and reports enhance usability; establishing their use in everyday clinical settings requires further simplification with a browser based interface which is currently under development.

COVE: a visual environment for ocean observatory design

Physical, chemical, and biological ocean processes play a crucial role in determining Earth's environment. Unfortunately, our knowledge of these processes is limited because oceanography is carried out today largely the way it was a century ago: as expeditionary science, going to sea in ships and measuring a relatively small number of parameters (e.g., temperature, salinity, and pressure) as time and budget allow. The NSF Ocean Observatories Initiative is a US$330 million project that will help transform oceanography from a data-poor to a data-rich science. A cornerstone of this project is the deep water Regional Scale Nodes (RSN) that will be installed off the coasts of Washington and Oregon. The RSN will include 1500 km of fiber optic cable providing power and bandwidth to the seafloor and throughout the water column. Thousands of sensors will be deployed to stream data and imagery to shore, where they will be available in real time for ocean scientists and the public at large. The design of the RSN is a complex undertaking, requiring a combination of many different interactive tools and areas of visualization: geographic visualization to see the available seafloor bathymetry, scientific visualization to examine existing geospatially located datasets, layout tools to place the sensors, and collaborative tools to communicate across the team during the design. COVE, the Common Observatory Visualization Environment, is a visualization environment designed to meet all these needs. COVE has been built by computer scientists working closely with the engineering and scientific teams who will build and use the RSN. This paper discusses the data and activities of cabled observatory design, the design of COVE, and results from its use across the team.

Analysis of gene expression by microarrays: cell biologist's gold mine or minefield?

The development of DNA microarrays to study simultaneously the level of mRNA expressed from thousands of genes offers great promise to cell biologists. Microarrays can be used to gain detailed information about transcriptional changes involved in a specific pathway, potentially leading to the identification of novel components of the signalling system. They can also be used to obtain a fingerprint of the transcriptional status of the cell under a given condition, which may be useful for characterising the pathways used in response to novel stimuli. The use of microarrays will generate huge amounts of expression data, contributing to the transformation of biology from a data-poor to a data-rich science. Whether this leads to real advances in the understanding of cell biological problems will depend on the development of methodologies, both in experimental biology and in bioinformatics, that allow meaningful knowledge to be extracted from all this information.

A point of contact between computer science and molecular biology

Molecular biology is rapidly becoming a data-rich science with extensive computational needs. The sheer volume of data poses a serious challenge in storing and retrieving biological information, and the rate of growth is exponential. Linking the heterogeneous data libraries of molecular biology, organizing its diverse and interrelated data sets, and developing effective query options for its databases are all areas for cross-fertilization between molecular biology and computer science. However, even the apparently simple task of analyzing a single sequence of DNA requires complex collaboration. For several years, we have been developing a computer toolkit for analyzing DNA sequences. The biology of gene regulation in mammals has driven the design of the sequence comparison toolkit to emphasize space-efficient algorithms with a high degree of sensitivity and has profoundly affected choice of tools and the development of algorithms. We sketch the biology of this class of problem and show how it specifically drives the software development. The main components of this toolkit are outlined.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>