scholarly journals Observational Open Science: An Application to the Literature on Irrelevant Events and Voting Behavior

2019 ◽  
Author(s):  
Matthew H. Graham ◽  
Gregory A Huber ◽  
neil malhotra ◽  
Cecilia Hyunjung Mo
Keyword(s):  
Voting Behavior ◽  
Observational Studies ◽  
Science Research ◽  
Open Science ◽  
Independent Data ◽  
Original Time Series ◽  
Ex Post ◽  
Specification Search ◽  
Science Framework ◽  
Original Time

Replication and transparency are increasingly important in bolstering the credibility of political science research, yet open science tools are typically designed for experiments. For observational studies, current replication practice suffers from an important pathology: just as researchers can often "p-hack" their way to initial findings, it is often possible to "null hack" findings away through specification and case search. We propose an observational open science framework that consists of extending the original time series, independent data collection, pre-registration, multiple simultaneous replications, and collaborators with mixed incentives. We apply the approach to three studies on "irrelevant" events and voting behavior. Each study replicates well in some areas and poorly in others. Had we sought to debunk any of the three with ex post specification search, we could have done so. However, our approach required us to see the full, complicated picture. We conclude with suggestions for future refinements to our approach.

Psychophysiology, cognition, and political differences

2021 ◽  
Vol 40 (2) ◽  
pp. 137-141 ◽  
Author(s):  
Jordan Mansell ◽  
Allison Harell ◽  
Elisabeth Gidengil ◽  
Patrick A. Stewart
Keyword(s):  
Political Science ◽  
Life Sciences ◽  
Science Research ◽  
Open Science ◽  
Common Theme ◽  
Special Issue ◽  
The Future ◽  
Research Questions ◽  
Political Differences ◽  
Science Framework

AbstractWe introduce the Politics and the Life Sciences special issue on Psychophysiology, Cognition, and Political Differences. This issue represents the second special issue funded by the Association for Politics and the Life Sciences that adheres to the Open Science Framework for registered reports (RR). Here pre-analysis plans (PAPs) are peer-reviewed and given in-principle acceptance (IPA) prior to data being collected and/or analyzed, and are published contingent upon the preregistration of the study being followed as proposed. Bound by a common theme of the importance of incorporating psychophysiological perspectives into the study of politics, broadly defined, the articles in this special issue feature a unique set of research questions and methodologies. In the following, we summarize the findings, discuss the innovations produced by this research, and highlight the importance of open science for the future of political science research.

scholarly journals Human Networks and Data Science (NSF 19-608): Uncovering the "dark matter" of research

2020 ◽  
Author(s):  
Brian A. Nosek ◽  
Stasa Milojevic ◽  
Valentin Pentchev ◽  
Xiaoran Yan ◽  
David M Litherland ◽  
...  
Keyword(s):  
Data Science ◽  
Science Research ◽  
Open Science ◽  
Heterogeneous Data ◽  
Data Sets ◽  
Heterogeneous Data Source ◽  
Science Of Science ◽  
Science Framework ◽  
Additional Support ◽  
Human Networks

With funding from the National Science Foundation, the Center for Open Science (COS) and Indiana University will create a dynamic, distributed, and heterogeneous data source for the advancement of science of science research. This will be achieved by using, enhancing, and combining the capabilities of the Open Science Framework (OSF) and the Collaborative Archive & Data Research Environment (CADRE). With over 200,000 users (currently growing by >220 per day), many thousands of projects, registrations, and papers, millions of files stored and managed, and rich metadata tracking researcher actions, the OSF is already a very rich dataset for investigating the research lifecycle, researcher behaviors, and how those behaviors evolve in the social network. As a cross-university effort, CADRE provides an integrated data mining and collaborative environment for big bibliographic data sets. While still under development, the CADRE platform has already attracted long-term financial commitments from 10 research intensive universities with additional support from multiple infrastructure and industry partners. Connecting these efforts will catalyze transformative research of human networks in the science of science.

scholarly journals Integrating Qualitative Methods and Open Science: Five Principles for More Trustworthy Research*

2021 ◽  
Author(s):  
Lee Humphreys ◽  
Neil A Lewis ◽  
Katherine Sender ◽  
Andrea Stevenson Won
Keyword(s):  
Knowledge Production ◽  
Ways Of Knowing ◽  
Science Research ◽  
Open Science ◽  
Communication Research ◽  
Competing Values ◽  
Open Communication ◽  
Qualitative And Quantitative ◽  
Science Framework ◽  
Equity And Inclusion

Abstract Recent initiatives toward open science in communication have prompted vigorous debate. In this article, we draw on qualitative and interpretive research methods to expand the key priorities that the open science framework addresses, namely producing trustworthy and quality research. This article contributes to communication research by integrating qualitative methodological literature with open communication science research to identify five broader commitments for all communication research: validity, transparency, ethics, reflexivity, and collaboration. We identify key opportunities where qualitative and quantitative communication scholars can leverage the momentum of open science to critically reflect on and improve our knowledge production processes. We also examine competing values that incentivize dubious practices in communication research, and discuss several metascience initiatives to enhance diversity, equity, and inclusion in our field and value multiple ways of knowing.

scholarly journals The STROBE extensions: protocol for a qualitative assessment of content and a survey of endorsement

BMJ Open ◽  
2017 ◽  
Vol 7 (10) ◽  
pp. e019043 ◽  
Author(s):  
Melissa K Sharp ◽  
Ana Utrobičić ◽  
Guadalupe Gómez ◽  
Erik Cobo ◽  
Elizabeth Wager ◽  
...  
Keyword(s):  
Observational Studies ◽  
Intraclass Correlation ◽  
Relevant Information ◽  
Open Science ◽  
Qualitative Assessment ◽  
Observational Research ◽  
Ethical Approval ◽  
Single Field ◽  
Broad Subject ◽  
Science Framework

IntroductionThe STrengthening the Reporting of OBservational studies in Epidemiology (STROBE) Statement was developed in response to inadequate reporting of observational studies. In recent years, several extensions to STROBE have been created to provide more nuanced field-specific guidance for authors. The content and the prevalence of extension endorsement have not yet been assessed. Accordingly, there are two aims: (1) to classify changes made in the extensions to identify strengths and weaknesses of the original STROBE checklist and (2) to determine the prevalence and typology of endorsement by journals in fields related to extensions.Methods and analysisTwo independent researchers will assess additions in each extension. Additions will be coded as ‘field specific’ (FS) or ‘not field specific’ (NFS). FS is defined as particularly relevant information for a single field and guidance provided generally cannot be extrapolated beyond that field. NFS is defined as information that reflects epidemiological or methodological tenets and can be generalised to most, if not all, types of observational research studies. Intraclass correlation will be calculated to measure reviewers’ concordance. On disagreement, consensus will be sought. Individual additions will be grouped by STROBE checklist items to identify the frequency and distribution of changes.Journals in fields related to extensions will be identified through National Library of Medicine PubMed Broad Subject Terms, screened for eligibility and further distilled via Ovid MEDLINE® search strategies for observational studies. Text describing endorsement will be extracted from each journal’s website. A classification scheme will be created for endorsement types and the prevalence of endorsement will be estimated. Analyses will use NVivo V.11 and SAS University Edition.Ethics and disseminationThis study does not require ethical approval as it does not involve human participants. This study has been preregistered on Open Science Framework.

Publikasi Akademik Sepanjang 2019

2019 ◽  
Author(s):  
Adib Rifqi Setiawan
Keyword(s):  
Open Science ◽  
Research Papers ◽  
Science Framework

Berikut ini beberapa publikasi saya pada 2019 ini. Penting atau tidak, saya menganggap bahwa publikasi hanyalah efek samping riset. Di luar publikasi ini, saya juga masih aktif sebagai penulis media daring, seperti Qureta.com, Selasar.com, dan SantriMilenial.net serta mengunggah beberapa artikel preprint melalui layanan Open Science Framework (OSF), EdArxiv.org, dan Research Papers in Economics (RePEc).

scholarly journals Superiority of Hybrid Soft Computing Models in Daily Suspended Sediment Estimation in Highly Dynamic Rivers

2021 ◽  
Vol 13 (2) ◽  
pp. 542
Author(s):  
Tarate Suryakant Bajirao ◽  
Pravendra Kumar ◽  
Manish Kumar ◽  
Ahmed Elbeltagi ◽  
Alban Kuriqi
Keyword(s):  
Time Series ◽  
Wavelet Transform ◽  
Suspended Sediment ◽  
River Basin ◽  
Time Series Data ◽  
Series Data ◽  
Discrete Wavelet ◽  
Original Time Series ◽  
Inference System ◽  
Original Time

Estimating sediment flow rate from a drainage area plays an essential role in better watershed planning and management. In this study, the validity of simple and wavelet-coupled Artificial Intelligence (AI) models was analyzed for daily Suspended Sediment (SSC) estimation of highly dynamic Koyna River basin of India. Simple AI models such as the Artificial Neural Network (ANN) and Adaptive Neuro-Fuzzy Inference System (ANFIS) were developed by supplying the original time series data as an input without pre-processing through a Wavelet (W) transform. The hybrid wavelet-coupled W-ANN and W-ANFIS models were developed by supplying the decomposed time series sub-signals using Discrete Wavelet Transform (DWT). In total, three mother wavelets, namely Haar, Daubechies, and Coiflets were employed to decompose original time series data into different multi-frequency sub-signals at an appropriate decomposition level. Quantitative and qualitative performance evaluation criteria were used to select the best model for daily SSC estimation. The reliability of the developed models was also assessed using uncertainty analysis. Finally, it was revealed that the data pre-processing using wavelet transform improves the model’s predictive efficiency and reliability significantly. In this study, it was observed that the performance of the Coiflet wavelet-coupled ANFIS model is superior to other models and can be applied for daily SSC estimation of the highly dynamic rivers. As per sensitivity analysis, previous one-day SSC (St-1) is the most crucial input variable for daily SSC estimation of the Koyna River basin.

Interactive Data Analysis of the 1980 Election

1983 ◽  
Vol 38 ◽  
pp. 2-3
Author(s):  
Jon A. Christopherson ◽  
Phillip J. Heyl
Keyword(s):  
Data Analysis ◽  
Voting Behavior ◽  
Social Science Research ◽  
Science Research ◽  
Coast Guard ◽  
Dartmouth College ◽  
Time Sharing ◽  
Batch Mode ◽  
Interactive Data Analysis ◽  
Interactive Data

By introducing data analysis techniques, analyzing current research in voting behavior, and using the computer for social science research, introductory American Government courses on the undergraduate level are undertaking ambitious goals. Using the SETUPS units for each presidential election since 1972, cadets at the Coast Guard Academy have engaged in a comprehensive approach to the study of American electoral behavior in an interactive data analysis mode. Although SETUPS modules are run at most colleges and universities in batch mode, there are imitations and drawbacks to batch mode. The Coast Guard Academy is able to utilize interactive data analysis as a result of its time-sharing arrangement with the Dartmouth College Time Sharing System (DCTS). In this article we will discuss the use of SETUPS in an interactive mode using the CIDAR interactive data analysis system.

scholarly journals Genetic and Transcriptomic Biomarkers in Neurodegenerative Diseases: Current Situation and the Road Ahead

Cells ◽  
2021 ◽  
Vol 10 (5) ◽  
pp. 1030
Author(s):  
Julie Lake ◽  
Catherine S. Storm ◽  
Mary B. Makarious ◽  
Sara Bandres-Ciga
Keyword(s):  
Neurodegenerative Diseases ◽  
Large Scale ◽  
Disease Diagnosis ◽  
Open Science ◽  
Future Directions ◽  
The Road ◽  
Current State ◽  
Molecular Complexity ◽  
Science Framework ◽  
Lateral Sclerosis

Neurodegenerative diseases are etiologically and clinically heterogeneous conditions, often reflecting a spectrum of disease rather than well-defined disorders. The underlying molecular complexity of these diseases has made the discovery and validation of useful biomarkers challenging. The search of characteristic genetic and transcriptomic indicators for preclinical disease diagnosis, prognosis, or subtyping is an area of ongoing effort and interest. The next generation of biomarker studies holds promise by implementing meaningful longitudinal and multi-modal approaches in large scale biobank and healthcare system scale datasets. This work will only be possible in an open science framework. This review summarizes the current state of genetic and transcriptomic biomarkers in Parkinson’s disease, Alzheimer’s disease, and amyotrophic lateral sclerosis, providing a comprehensive landscape of recent literature and future directions.

scholarly journals Published patterns of spin in biomedical literature: a protocol for a meta-research study

BMJ Open ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. e043784
Author(s):  
Naichuan Su ◽  
Michiel van der Linden ◽  
Geert JMG van der Heijden ◽  
Stefan Listl ◽  
Stefan Schandelmaier ◽  
...  
Keyword(s):  
Systematic Reviews ◽  
Scientific Journal ◽  
Open Science ◽  
Biomedical Literature ◽  
Measurement Tool ◽  
Delphi Consensus ◽  
Reporting Practices ◽  
Literature Reviews ◽  
Science Framework ◽  
Meta Analyses

IntroductionSpin is defined as reporting practices that distort the interpretation of results and create misleading conclusions by suggesting more favourable results. Such unjustifiable and misleading misrepresentation may negatively influence the development of further studies, clinical practice and healthcare policies. Spin manifests in various patterns in different sections of publications (titles, abstracts and main texts). The primary aim of this study is to identify reported spin patterns and assess the prevalence of spin in general, and the prevalence of spin patterns reported in biomedical literature based on previously published systematic reviews and literature reviews on spin.Methods and analysisPubMed, EMBASE and SCOPUS will be searched to identify systematic or literature reviews on spin in biomedicine. To improve the comprehensiveness of the search, the snowballing method will be used to broaden the search. The data on spin-related outcomes and characteristics of the included studies will be extracted. The methodological quality of the included studies will be assessed with selective items of the A MeaSurement Tool to Assess systematic Reviews-2 checklist. A new classification scheme for spin patterns will be developed if the classifications of spin patterns identified in the included studies vary. The prevalence of spin and spin patterns will be pooled based on meta-analyses if the classification schemes for spin are comparable across included studies. Otherwise, the prevalence will be described qualitatively. The seriousness of spin patterns will be assessed based on a Delphi consensus study.Ethics and disseminationThis study has been approved by the Academic Centre for Dentistry Amsterdam Ethics Review Committee (2020250). The study will be submitted to a peer-reviewed scientific journal.RegistrationOpen Science Framework: osf.io/hzv6e

scholarly journals The Systematic Bias of Entropy Calculation in the Multi-Scale Entropy Algorithm

Entropy ◽  
2021 ◽  
Vol 23 (6) ◽  
pp. 659
Author(s):  
Jue Lu ◽  
Ze Wang
Keyword(s):  
Time Series ◽  
Standard Deviation ◽  
Coarse Graining ◽  
Sample Entropy ◽  
Systematic Bias ◽  
Original Time Series ◽  
Multi Scale ◽  
Calculation Results ◽  
Surrogate Measure ◽  
Original Time

Entropy indicates irregularity or randomness of a dynamic system. Over the decades, entropy calculated at different scales of the system through subsampling or coarse graining has been used as a surrogate measure of system complexity. One popular multi-scale entropy analysis is the multi-scale sample entropy (MSE), which calculates entropy through the sample entropy (SampEn) formula at each time scale. SampEn is defined by the “logarithmic likelihood” that a small section (within a window of a length m) of the data “matches” with other sections will still “match” the others if the section window length increases by one. “Match” is defined by a threshold of r times standard deviation of the entire time series. A problem of current MSE algorithm is that SampEn calculations at different scales are based on the same matching threshold defined by the original time series but data standard deviation actually changes with the subsampling scales. Using a fixed threshold will automatically introduce systematic bias to the calculation results. The purpose of this paper is to mathematically present this systematic bias and to provide methods for correcting it. Our work will help the large MSE user community avoiding introducing the bias to their multi-scale SampEn calculation results.

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