Evidence map›Paper›PMID 37009525›Full record

ArticleInformation (Basel)2022

SOCRAT: a Dynamic Web Toolbox for Interactive Data Processing, Analysis and Visualization.

Alexandr A Kalinin, Selvam Palanimalai, Junqi Zhu, Wenyi Wu, Nikhil Devraj, Chunchun Ye, Nellie Ponarul, Syed S Husain, Ivo D Dinov

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Information (Basel), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Alexandr A KalininStatistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48104, USA.
Selvam PalanimalaiStatistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48104, USA.
Junqi ZhuStatistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48104, USA.
Wenyi WuStatistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48104, USA.
Nikhil DevrajStatistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48104, USA.
Chunchun YeStatistics Online Computational Resource (SOCR), Department of Statistics, University of California Los Angeles, Los Angeles, CA 90095, USA.
Nellie PonarulStatistics Online Computational Resource (SOCR), Department of Statistics, University of California Los Angeles, Los Angeles, CA 90095, USA.
Syed S HusainStatistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48104, USA.
Ivo D DinovStatistics Online Computational Resource (SOCR), Department of Health Behavior and Biological Sciences, University of Michigan, Ann Arbor, MI 48104, USA.

Funding

Michigan Institute for Clinical and Health Research (MICHR)UL1TR002240 · NCATS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LUMENG, JULIE C, MASHOUR, GEORGE ALEXANDER · 2017 to 2022
$54.9M
Pilot and Feasibility (P and F) ProgramP30DK089503 · NIDDK · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Jeffrey F Horowitz · 2010 to 2026
$20.3M
COVID-19 supplement to a computational examination of threat and reward constructs in a predominantly low-income, longitudinal sample at increased risk for internalizing disordersR01MH121079 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI HYDE, LUKE WILLIAMSON, MITCHELL, COLTER M.S. · 2019 to 2024
$7.2M
Digital behavioral phenotyping and multi-region electrophysiology to determine behavioral and neural network changes underlying the stress response in miceR01MH126137 · NIMH · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI WATSON, BRENDON O · 2021 to 2025
$3.3M
Biomedical Informatics and Data Science Training Program (BIDS-TP)T32GM141746 · NIGMS · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI Ivo D Dinov, RYAN E MILLS · 2021 to 2026
$2.6M
Optimal Decision Making in Radiotherapy Using Panomics AnalyticsR01CA233487 · NCI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI EL NAQA, ISSAM M. · 2019 to 2023
$2.4M
Pilot Projects CoreP20NR015331 · NINR · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI LARSON, JANET L. · 2014 to 2018
$1.3M
NCATS NIH HHS UL1 TR002240NCI NIH HHS R01 CA233487NIDDK NIH HHS P30 DK089503NIGMS NIH HHS T32 GM141746NIMH NIH HHS R01 MH121079NIMH NIH HHS R01 MH126137NINR NIH HHS P20 NR015331
6 · The paper itself

Abstract

Many systems for exploratory and visual data analytics require platform-dependent software installation, coding skills, and analytical expertise. The rapid advances in data-acquisition, web-based information, and communication and computation technologies promoted the explosive growth of online services and tools implementing novel solutions for interactive data exploration and visualization. However, web-based solutions for visual analytics remain scattered and relatively problem-specific. This leads to per-case re-implementations of common components, system architectures, and user interfaces, rather than focusing on innovation and building sophisticated applications for visual analytics. In this paper, we present the Statistics Online Computational Resource Analytical Toolbox (SOCRAT), a dynamic, flexible, and extensible web-based visual analytics framework. The SOCRAT platform is designed and implemented using multi-level modularity and declarative specifications. This enables easy integration of a number of components for data management, analysis, and visualization. SOCRAT benefits from the diverse landscape of existing in-browser solutions by combining them with flexible template modules into a unique, powerful, and feature-rich visual analytics toolbox. The platform integrates a number of independently developed tools for data import, display, storage, interactive visualization, statistical analysis, and machine learning. Various use cases demonstrate the unique features of SOCRAT for visual and statistical analysis of heterogeneous types of data.

Indexed as

exploratory analysisstatistical visualizationvisual analyticsweb toolkits

Identifiers

PMID37009525
PMCPMC10062429

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.