scholarly journals ORSO (Online Resource for Social Omics): A data-driven social network connecting scientists to genomics datasets

2020 ◽  
Vol 16 (1) ◽  
pp. e1007571
Author(s):  
Christopher A. Lavender ◽  
Andrew J. Shapiro ◽  
Frank S. Day ◽  
David C. Fargo
Keyword(s):  
Social Network ◽  
Data Driven ◽  
Online Resource

scholarly journals The EBRAINS NeuroFeatureExtract: An Online Resource for the Extraction of Neural Activity Features From Electrophysiological Data

2021 ◽  
Vol 15 ◽  
Author(s):  
Luca L. Bologna ◽  
Roberto Smiriglia ◽  
Dario Curreri ◽  
Michele Migliore
Keyword(s):  
Neural Activity ◽  
Ad Hoc ◽  
Neural Model ◽  
Data Driven ◽  
Neural Dynamics ◽  
Model Optimization ◽  
Activity Data ◽  
Online Resource ◽  
Public Repositories ◽  
Scientific Questions

The description of neural dynamics, in terms of precise characterizations of action potential timings and shape and voltage related measures, is fundamental for a deeper understanding of the neural code and its information content. Not only such measures serve the scientific questions posed by experimentalists but are increasingly being used by computational neuroscientists for the construction of biophysically detailed data-driven models. Nonetheless, online resources enabling users to perform such feature extraction operation are lacking. To address this problem, in the framework of the Human Brain Project and the EBRAINS research infrastructure, we have developed and made available to the scientific community the NeuroFeatureExtract, an open-access online resource for the extraction of electrophysiological features from neural activity data. This tool allows to select electrophysiological traces of interest, fetched from public repositories or from users’ own data, and provides ad hoc functionalities to extract relevant features. The output files are properly formatted for further analysis, including data-driven neural model optimization.

scholarly journals No strong evidence that social network index is associated with gray matter volume from a data-driven investigation

Cortex ◽  
2020 ◽  
Vol 125 ◽  
pp. 307-317 ◽  
Author(s):  
Chujun Lin ◽  
Umit Keles ◽  
J. Michael Tyszka ◽  
Marcos Gallo ◽  
Lynn Paul ◽  
...  
Keyword(s):  
Social Network ◽  
Gray Matter ◽  
Strong Evidence ◽  
Gray Matter Volume ◽  
Data Driven ◽  
Matter Volume

scholarly journals Data-Driven Computational Social Network Science: Predictive and Inferential Models for Web-Enabled Scientific Discoveries

2021 ◽  
Vol 4 ◽  
Author(s):  
Frank Emmert-Streib ◽  
Matthias Dehmer
Keyword(s):  
Social Networks ◽  
Social Network ◽  
Social Theory ◽  
Network Science ◽  
Prediction Models ◽  
Traditional Approach ◽  
Computational Social Science ◽  
Data Driven ◽  
Social Data ◽  
Causal Explanations

The ultimate goal of the social sciences is to find a general social theory encompassing all aspects of social and collective phenomena. The traditional approach to this is very stringent by trying to find causal explanations and models. However, this approach has been recently criticized for preventing progress due to neglecting prediction abilities of models that support more problem-oriented approaches. The latter models would be enabled by the surge of big Web-data currently available. Interestingly, this problem cannot be overcome with methods from computational social science (CSS) alone because this field is dominated by simulation-based approaches and descriptive models. In this article, we address this issue and argue that the combination of big social data with social networks is needed for creating prediction models. We will argue that this alliance has the potential for gradually establishing a causal social theory. In order to emphasize the importance of integrating big social data with social networks, we call this approach data-driven computational social network science (DD-CSNS).

scholarly journals Social Network Analysis, Ethics and LGPD, considerations in research

2021 ◽  
Vol 14 (2) ◽  
pp. 28-52
Author(s):  
Luiz Paulo Carvalho ◽  
Jonice Oliveira ◽  
Flávia Maria Santoro ◽  
Claudia Cappelli
Keyword(s):  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Knowledge Construction ◽  
Personal Data ◽  
Research Area ◽  
Research Field ◽  
Data Driven ◽  
Brazilian Society ◽  
The Social

Data protection and data-driven solutions are two progressing areas permeating Brazilian society. This work presents an interdisciplinary theoretical approach related to Ethics, from the ethics in computing perspective; the LGPD, from the Law studies perspective; and the Social Network Analysis in Brazil, from the Informatics perspective. This research area utilizes personal data extensively for knowledge construction, with semantic contributions, analyzing the reality; or pragmatic, building artifacts. Challenges and inseparable issues are observed, exposed, and debated in this work. We present considerations combining the three topics, personal data in the research field of social networks in Brazil respecting the LGPD and ethics precepts.

Analyzing enterprise information system’s feature use: a data-driven perspective

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Yu Jia ◽  
Shilun Ge ◽  
Nianxin Wang
Keyword(s):  
Information System ◽  
Social Network ◽  
Social Network Analysis ◽  
Network Analysis ◽  
Data Driven ◽  
Power Law Distribution ◽  
Enterprise Information System ◽  
Enterprise Information ◽  
Content Type ◽  
User Groups

PurposeThe purpose of this study is to propose a data-driven perspective to analyze enterprise information system (EIS) feature use for understanding what and how the system features are actually used in the organization.Design/methodology/approachAn empirical study was conducted by analyzing information system (IS) log data collected from a well-known shipbuilding manufacturer in China. The multiple analytical approach employed in this study includes social network analysis, association rules learning and human dynamics.FindingsThis study first classified IS users into 41 core users and 325 general users. Then 24 core modules and 54 general modules were identified by social network analysis, and the correlation between them was analyzed. Finally, we found that the IS use time intervals for different user groups followed a power-law distribution, and IS use displayed strong burstiness and weak memory.Originality/valueThis study proposes a data-driven perspective to investigate how the system features are actually used in the organization. This study contributes to the literature and opens a new avenue for future IS use research. Furthermore, this study informs managers how to diagnose, maintain and optimize the implemented IS in order to maximize IS benefits.

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