ArticleIEEE computer graphics and applications
DIVE: a graph-based visual-analytics framework for big data.
Article in IEEE computer graphics and applications. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Molecular Dynamics Methods for Antibody Design.Methods in molecular biology (Clifton, N.J.) · 2023Article
- Tumorigenic p53 mutants undergo common structural disruptions including conversion to α-sheet structure.Protein science : a publication of the Protein Society · 2020Article
- Semantics for an Integrative and Immersive Pipeline Combining Visualization and Analysis of Molecular Data.Journal of integrative bioinformatics · 2018Article
- SmartR: an open-source platform for interactive visual analytics for translational research data.Bioinformatics (Oxford, England) · 2017Article
- An in silico algorithm for identifying stabilizing pockets in proteins: test case, the Y220C mutant of the p53 tumor suppressor protein.Protein engineering, design & selection : PEDS · 2016Article
- High-resolution visualisation of the states and pathways sampled in molecular dynamics simulations.Scientific reports · 2014Article
- DIVE: a data intensive visualization engine.Bioinformatics (Oxford, England) · 2014Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The need for data-centric scientific tools is growing; domains such as biology, chemistry, and physics are increasingly adopting computational approaches. So, scientists must deal with the challenges of big data. To address these challenges, researchers built a visual-analytics platform named DIVE (Data Intensive Visualization Engine). DIVE is a data-agnostic, ontologically expressive software framework that can stream large datasets at interactive speeds. In particular, DIVE makes novel contributions to structured-data-model manipulation and high-throughput streaming of large, structured datasets.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.