scholarly journals A Comprehensive, FAIR File Format for Neuroanatomical Structure Modeling

2021 ◽  
Author(s):  
A. E. Sullivan ◽  
S. J. Tappan ◽  
P. J. Angstman ◽  
A. Rodriguez ◽  
G. C. Thomas ◽  
...  
Keyword(s):  
Scientific Data ◽  
Biological Research ◽  
File Format ◽  
Data Types ◽  
Alternative Analysis ◽  
Digital Reconstruction ◽  
Data Files ◽  
Rich Data ◽  
Scientific Questions ◽  
Neuroscience Community

AbstractWith advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, the Neuromorphological File Specification, available at www.mbfbioscience.com/filespecification (Angstman et al., 2020). The format, created and maintained by MBF Bioscience, is broadly utilized by the neuroscience community. The data format’s structure and capabilities have evolved since its inception, with modifications made to keep pace with advancements in microscopy and the scientific questions raised by worldwide experts in the field. More recent modifications to the neuromorphological file format ensure it abides by the Findable, Accessible, Interoperable, and Reusable (FAIR) data principles promoted by the International Neuroinformatics Coordinating Facility (INCF; Wilkinson et al., Scientific Data, 3, 160018,, 2016). The incorporated metadata make it easy to identify and repurpose these data types for downstream applications and investigation. This publication describes key elements of the file format and details their relevant structural advantages in an effort to encourage the reuse of these rich data files for alternative analysis or reproduction of derived conclusions.

scholarly journals A comprehensive, FAIR file format for neuroanatomical structure modeling

2020 ◽  
Author(s):  
A. E. Sullivan ◽  
S. J. Tappan ◽  
P. J. Angstman ◽  
A. Rodriguez ◽  
G. C. Thomas ◽  
...  
Keyword(s):  
Biological Research ◽  
File Format ◽  
Data Types ◽  
Alternative Analysis ◽  
Data Format ◽  
Digital Reconstruction ◽  
Data Files ◽  
Rich Data ◽  
Scientific Questions ◽  
Neuroscience Community

AbstractWith advances in microscopy and computer science, the technique of digitally reconstructing, modeling, and quantifying microscopic anatomies has become central to many fields of biological research. MBF Bioscience has chosen to openly document their digital reconstruction file format, Neuromorphological File Specification (4.0), available at www.mbfbioscience.com/filespecification (Angstman et al. 2020). One of such technologies, the format created and maintained by MBF Bioscience is broadly utilized by the neuroscience community. The data format’s structure and capabilities have evolved since its inception, with modifications made to keep pace with advancements in microscopy and the scientific questions raised by worldwide experts in the field. More recent modifications to the neuromorphological data format ensure it abides by the Findable, Accessible, Interoperable, and Reusable (FAIR) data standards promoted by the International Neuroinformatics Coordinating Facility (INCF; Wilkinson et al. 2016). The incorporated metadata make it easy to identify and repurpose these data types for downstream application and investigation. This publication describes key elements of the file format and details their relevant structural advantages in an effort to encourage the reuse of these rich data files for alternative analysis or reproduction of derived conclusions.

scholarly journals LandScape: a web application for interactive genomic summary visualization

2019 ◽  
Author(s):  
Wenlong Jia ◽  
Hechen Li ◽  
Shiying Li ◽  
Shuaicheng Li
Keyword(s):  
Genetic Information ◽  
Web Application ◽  
Genomic Research ◽  
File Format ◽  
Data Types ◽  
Web Based ◽  
Link Type ◽  
Level Data ◽  
Real Time Visualization ◽  
Information Landscape

ABSTRACTSummaryVisualizing integrated-level data from genomic research remains a challenge, as it requires sufficient coding skills and experience. Here, we present LandScapeoviz, a web-based application for interactive and real-time visualization of summarized genetic information. LandScape utilizes a well-designed file format that is capable of handling various data types, and offers a series of built-in functions to customize the appearance, explore results, and export high-quality diagrams that are available for publication.Availability and implementationLandScape is deployed at bio.oviz.org/demo-project/analyses/landscape for online use. Documentation and demo data are freely available on this website and GitHub (github.com/Nobel-Justin/Oviz-Bio-demo)[email protected]

scholarly journals The Association of Religion Data Archives (ARDA): Online Research Data, Tools and References

2008 ◽  
Vol 1 (3) ◽  
pp. 456-470 ◽  
Author(s):  
Roger Finke ◽  
Amy Adamczyk
Keyword(s):  
State University ◽  
Pennsylvania State University ◽  
Research And Teaching ◽  
Data Archives ◽  
Wide Range ◽  
International Data ◽  
Data Files ◽  
The Rich ◽  
Rich Data ◽  
Website Features

AbstractThe Association of Religion Data Archives (ARDA) currently archives over 500 local, national, and international data files, and offers a wide range of research tools to build surveys, preview data online, develop customized maps and reports of United States church membership, and examine religion differences across nations and regions of the world. The ARDA also supports reference and teaching tools that draw on the rich data archive. This research article offers a brief introduction to the quantitative data available for exploration or download, and a few of the website features most useful for research and teaching. Supported by the Lilly Endowment, the John Templeton Foundation, the Pennsylvania State University, and the Baylor Institute for Studies of Religion, all data downloads and online services are free of charge.

scholarly journals New features and improvements in the NEA nuclear data tool suite

2020 ◽  
Vol 239 ◽  
pp. 19003
Author(s):  
M. Fleming ◽  
I. Hill ◽  
J. Dyrda ◽  
L. Fiorito ◽  
N. Soppera ◽  
...  
Keyword(s):  
Relational Databases ◽  
Uncertainty Propagation ◽  
Nuclear Data ◽  
Data Types ◽  
Reactor Physics ◽  
Energy Agency ◽  
Order Of Magnitude ◽  
Data Files ◽  
Improved Performance ◽  
Data Libraries

The OECD Nuclear Energy Agency (NEA) has developed and maintains several products that are used in the verification and validation of nuclear data, including the Java-based Nuclear Data Information System (JANIS) and the Nuclear Data Sensitivity Tool (NDaST). These integrate other collections of the NEA, including the International Handbooks of benchmark experiments on Criticality Safety and Reactor Physics (ICSBEP and IRPhEP) and their supporting relational databases (DICE and IDAT). Recent development of the JANIS, DICE and NDaST systems have resulted in the ability to perform uncertainty propagation utilising Legendre polynomial sensitivities, calculation of case-to-case covariances and correlations, use of spectrum weighting in perturbations, calculation of statistical results with suites of randomly sampled nuclear data files and new command-line interfaces to automate analyses and generate XML outputs. All of the most recent, major nuclear data libraries have been fully processed and incorporated, along with new visualisation features for covariances and sensitivities, an expanded set of reaction channel definitions, and new EXFOR data types defined by the NRDC. Optimisation of numerical methods has also improved performance, with over order-of-magnitude speed-up in the case of sensitivity-uncertainty calculations.

scholarly journals Graduate Curriculum for Biological Information Specialists: A Key to Integration of Scale in Biology

2008 ◽  
Vol 2 (2) ◽  
pp. 31-40 ◽  
Author(s):  
Carole L. Palmer ◽  
Bryan P. Heidorn ◽  
Dan Wright ◽  
Melissa H. Cragin
Keyword(s):  
Biological Sciences ◽  
Multiple Scales ◽  
Scientific Information ◽  
Scientific Data ◽  
High Rate ◽  
Biological Information ◽  
Biological Research ◽  
Multi Scale ◽  
Graduate Curriculum ◽  
Professional Capacity

Scientific data problems do not stand in isolation. They are part of a larger set of challenges associated with the escalation of scientific information and changes in scholarly communication in the digital environment. Biologists in particular are generating enormous sets of data at a high rate, and new discoveries in the biological sciences will increasingly depend on the integration of data across multiple scales. This work will require new kinds of information expertise in key areas. To build this professional capacity we have developed two complementary educational programs: a Biological Information Specialist (BIS) masters degree and a concentration in Data Curation (DC). We believe that BISs will be central in the development of cyberinfrastructure and information services needed to facilitate interdisciplinary and multi-scale science. Here we present three sample cases from our current research projects to illustrate areas in which we expect information specialists to make important contributions to biological research practice.

Identifying and solving scientific problems in the medicine: key to become a competent scientist

2020 ◽  
Vol 45 (2) ◽  
Author(s):  
Duygu Aydemir ◽  
Nuriye Nuray Ulusu
Keyword(s):  
Lifelong Learning ◽  
Scientific Knowledge ◽  
Scientific Method ◽  
Scientific Research ◽  
Scientific Data ◽  
Crucial Step ◽  
Scientific Problem ◽  
Multifactorial Diseases ◽  
Scientific Questions ◽  
Day By Day

AbstractThe scientific method can be described as a multistep and detailed process, in which finding the best question is the first and most crucial step. Thus, scientific problem should be examined thoroughly in different ways and perspectives. The amount and diversity of scientific data are enormously increasing and becoming more specific day by day, therefore traditional observational biology is not sufficient on an individual basis to understand and treat multifactorial diseases. Moreover, protocols, documentations, information, outcomes, precisions, and considerations of evidence should be improved to answer scientific questions correctly during the scientific research. Because of the diversity of the data and the methods, statisticians and methodologists should be involved and contribute to the all stages of research. Besides that, all scientific data should be certainly reproducible and repeatable. Scientific knowledge is in a state of flux and becomes more complex day by day. Thus, becoming a competent scientist needs, abilities and skills such as creativity, hardworking and self-discipline that all requires lifelong learning, searching, and widening scientific horizons consistently.

The BioDynaMo Project

2021 ◽  
pp. 1785-1791
Author(s):  
Roman Bauer ◽  
Lukas Breitwieser ◽  
Alberto Di Meglio ◽  
Leonard Johard ◽  
Marcus Kaiser ◽  
...  
Keyword(s):  
Computer Simulations ◽  
Lessons Learned ◽  
Biological Research ◽  
Computing Systems ◽  
Spatial And Temporal Scales ◽  
Scientific Investigations ◽  
Complex Organization ◽  
Computer Resources ◽  
Scientific Questions ◽  
Heterogeneous Interactions

Computer simulations have become a very powerful tool for scientific research. Given the vast complexity that comes with many open scientific questions, a purely analytical or experimental approach is often not viable. For example, biological systems comprise an extremely complex organization and heterogeneous interactions across different spatial and temporal scales. In order to facilitate research on such problems, the BioDynaMo project aims at a general platform for computer simulations for biological research. Since scientific investigations require extensive computer resources, this platform should be executable on hybrid cloud computing systems, allowing for the efficient use of state-of-the-art computing technology. This chapter describes challenges during the early stages of the software development process. In particular, we describe issues regarding the implementation and the highly interdisciplinary as well as international nature of the collaboration. Moreover, we explain the methodologies, the approach, and the lessons learned by the team during these first stages.

HDinHD: A Rich Data Portal for Huntington’s Disease Research

2021 ◽  
pp. 1-8
Author(s):  
Jeff Aaronson ◽  
Vahri Beaumont ◽  
Richard A. Blevins ◽  
Viktoria Andreeva ◽  
Irina Murasheva ◽  
...  
Keyword(s):  
Huntington's Disease ◽  
Huntington’S Disease ◽  
Scientific Data ◽  
High Definition ◽  
Related Data ◽  
Online Portal ◽  
Wide Range ◽  
Data Portal ◽  
Rich Data

HDinHD (Huntington’s Disease in High Definition; HDinHD.org) is an open online portal for the HD research community that presents a synthesized view of HD-related scientific data. Here, we present a broad overview of HDinHD and highlight the newly launched HDinHD Explorer tool that enables researchers to discover and explore a wide range of diverse yet interconnected HD-related data. We demonstrate the utility of HDinHD Explorer through data mining of a single collection of newly released in vivo therapeutic intervention study reports alongside previously published reports.

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