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Relevant bibliographies by topics / Collaborative Reproducible Research

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Academic literature on the topic 'Collaborative Reproducible Research'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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Journal articles on the topic "Collaborative Reproducible Research"

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Langer, Steve G., George Shih, Paul Nagy, and Bennet A. Landman. "Collaborative and Reproducible Research: Goals, Challenges, and Strategies." Journal of Digital Imaging 31, no. 3 (2018): 275–82. http://dx.doi.org/10.1007/s10278-017-0043-x.

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Abstract Combining imaging biomarkers with genomic and clinical phenotype data is the foundation of precision medicine research efforts. Yet, biomedical imaging research requires unique infrastructure compared with principally text-driven clinical electronic medical record (EMR) data. The issues are related to the binary nature of the file format and transport mechanism for medical images as well as the post-processing image segmentation and registration needed to combine anatomical and physiological imaging data sources. The SiiM Machine Learning Committee was formed to analyze the gaps and challenges surrounding research into machine learning in medical imaging and to find ways to mitigate these issues. At the 2017 annual meeting, a whiteboard session was held to rank the most pressing issues and develop strategies to meet them. The results, and further reflections, are summarized in this paper.

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Langer, Steve G., George Shih, Paul Nagy, and Bennet A. Landman. "Correction to: Collaborative and Reproducible Research: Goals, Challenges, and Strategies." Journal of Digital Imaging 32, no. 5 (2019): 897. http://dx.doi.org/10.1007/s10278-018-0164-x.

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Yaniv, Ziv, Bradley C. Lowekamp, Hans J. Johnson, and Richard Beare. "SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research." Journal of Digital Imaging 31, no. 3 (2017): 290–303. http://dx.doi.org/10.1007/s10278-017-0037-8.

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Abstract Modern scientific endeavors increasingly require team collaborations to construct and interpret complex computational workflows. This work describes an image-analysis environment that supports the use of computational tools that facilitate reproducible research and support scientists with varying levels of software development skills. The Jupyter notebook web application is the basis of an environment that enables flexible, well-documented, and reproducible workflows via literate programming. Image-analysis software development is made accessible to scientists with varying levels of programming experience via the use of the SimpleITK toolkit, a simplified interface to the Insight Segmentation and Registration Toolkit. Additional features of the development environment include user friendly data sharing using online data repositories and a testing framework that facilitates code maintenance. SimpleITK provides a large number of examples illustrating educational and research-oriented image analysis workflows for free download from GitHub under an Apache 2.0 license: github.com/InsightSoftwareConsortium/SimpleITK-Notebooks.

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Lang, Konrad, Sarah Stryeck, David Bodruzic, et al. "CyVerse Austria—A Local, Collaborative Cyberinfrastructure." Mathematical and Computational Applications 25, no. 2 (2020): 38. http://dx.doi.org/10.3390/mca25020038.

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Life sciences (LS) are advanced in research data management, since LS have established disciplinary tools for data archiving as well as metadata standards for data reuse. However, there is a lack of tools supporting the active research process in terms of data management and data analytics. This leads to tedious and demanding work to ensure that research data before and after publication are FAIR (findable, accessible, interoperable and reusable) and that analyses are reproducible. The initiative CyVerse US from the University of Arizona, US, supports all processes from data generation, management, sharing and collaboration to analytics. Within the presented project, we deployed an independent instance of CyVerse in Graz, Austria (CAT) in frame of the BioTechMed association. CAT helped to enhance and simplify collaborations between the three main universities in Graz. Presuming steps were (i) creating a distributed computational and data management architecture (iRODS-based), (ii) identifying and incorporating relevant data from researchers in LS and (iii) identifying and hosting relevant tools, including analytics software to ensure reproducible analytics using Docker technology for the researchers taking part in the initiative. This initiative supports research-related processes, including data management and analytics for LS researchers. It also holds the potential to serve other disciplines and provides potential for Austrian universities to integrate their infrastructure in the European Open Science Cloud.

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Yaniv, Ziv, Bradley C. Lowekamp, Hans J. Johnson, and Richard Beare. "Correction to: SimpleITK Image-Analysis Notebooks: a Collaborative Environment for Education and Reproducible Research." Journal of Digital Imaging 32, no. 6 (2019): 1118. http://dx.doi.org/10.1007/s10278-018-0165-9.

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Fursin, Grigori, Renato Miceli, Anton Lokhmotov, et al. "Collective Mind: Towards Practical and Collaborative Auto-Tuning." Scientific Programming 22, no. 4 (2014): 309–29. http://dx.doi.org/10.1155/2014/797348.

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Empirical auto-tuning and machine learning techniques have been showing high potential to improve execution time, power consumption, code size, reliability and other important metrics of various applications for more than two decades. However, they are still far from widespread production use due to lack of native support for auto-tuning in an ever changing and complex software and hardware stack, large and multi-dimensional optimization spaces, excessively long exploration times, and lack of unified mechanisms for preserving and sharing of optimization knowledge and research material. We present a possible collaborative approach to solve above problems using Collective Mind knowledge management system. In contrast with previous cTuning framework, this modular infrastructure allows to preserve and share through the Internet the whole auto-tuning setups with all related artifacts and their software and hardware dependencies besides just performance data. It also allows to gradually structure, systematize and describe all available research material including tools, benchmarks, data sets, search strategies and machine learning models. Researchers can take advantage of shared components and data with extensible meta-description to quickly and collaboratively validate and improve existing auto-tuning and benchmarking techniques or prototype new ones. The community can now gradually learn and improve complex behavior of all existing computer systems while exposing behavior anomalies or model mispredictions to an interdisciplinary community in a reproducible way for further analysis. We present several practical, collaborative and model-driven auto-tuning scenarios. We also decided to release all material atc-mind.org/repoto set up an example for a collaborative and reproducible research as well as our new publication model in computer engineering where experimental results are continuously shared and validated by the community.

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Finak, Greg, Bryan Mayer, William Fulp, et al. "DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis." Gates Open Research 2 (June 22, 2018): 31. http://dx.doi.org/10.12688/gatesopenres.12832.1.

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A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code and primary data be made available with publications. There has been little practical advice on implementing reproducible research work-flows for large ’omics’ or systems biology data sets used by teams of analysts working in collaboration. In such instances it is important to ensure all analysts use the same version of a data set for their analyses. Yet, instantiating relational databases and standard operating procedures can be unwieldy, with high "startup" costs and poor adherence to procedures when they deviate substantially from an analyst’s usual work-flow. Ideally a reproducible research work-flow should fit naturally into an individual’s existing work-flow, with minimal disruption. Here, we provide an overview of how we have leveraged popular open source tools, including Bioconductor, Rmarkdown, git version control, R, and specifically R’s package system combined with a new tool DataPackageR, to implement a lightweight reproducible research work-flow for preprocessing large data sets, suitable for sharing among small-to-medium sized teams of computational scientists. Our primary contribution is the DataPackageR tool, which decouples time-consuming data processing from data analysis while leaving a traceable record of how raw data is processed into analysis-ready data sets. The software ensures packaged data objects are properly documented and performs checksum verification of these along with basic package version management, and importantly, leaves a record of data processing code in the form of package vignettes. Our group has implemented this work-flow to manage, analyze and report on pre-clinical immunological trial data from multi-center, multi-assay studies for the past three years.

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Finak, Greg, Bryan Mayer, William Fulp, et al. "DataPackageR: Reproducible data preprocessing, standardization and sharing using R/Bioconductor for collaborative data analysis." Gates Open Research 2 (July 10, 2018): 31. http://dx.doi.org/10.12688/gatesopenres.12832.2.

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A central tenet of reproducible research is that scientific results are published along with the underlying data and software code necessary to reproduce and verify the findings. A host of tools and software have been released that facilitate such work-flows and scientific journals have increasingly demanded that code and primary data be made available with publications. There has been little practical advice on implementing reproducible research work-flows for large ’omics’ or systems biology data sets used by teams of analysts working in collaboration. In such instances it is important to ensure all analysts use the same version of a data set for their analyses. Yet, instantiating relational databases and standard operating procedures can be unwieldy, with high "startup" costs and poor adherence to procedures when they deviate substantially from an analyst’s usual work-flow. Ideally a reproducible research work-flow should fit naturally into an individual’s existing work-flow, with minimal disruption. Here, we provide an overview of how we have leveraged popular open source tools, including Bioconductor, Rmarkdown, git version control, R, and specifically R’s package system combined with a new tool DataPackageR, to implement a lightweight reproducible research work-flow for preprocessing large data sets, suitable for sharing among small-to-medium sized teams of computational scientists. Our primary contribution is the DataPackageR tool, which decouples time-consuming data processing from data analysis while leaving a traceable record of how raw data is processed into analysis-ready data sets. The software ensures packaged data objects are properly documented and performs checksum verification of these along with basic package version management, and importantly, leaves a record of data processing code in the form of package vignettes. Our group has implemented this work-flow to manage, analyze and report on pre-clinical immunological trial data from multi-center, multi-assay studies for the past three years.

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Considine, Michael, Hilary Parker, Yingying Wei, et al. "AGA: Interactive pipeline for reproducible genomics analyses." F1000Research 4 (January 28, 2015): 28. http://dx.doi.org/10.12688/f1000research.6030.1.

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Automated Genomics Analysis (AGA) is an interactive program to analyze high-throughput genomic data sets on a variety of platforms. An easy to use, point and click, guided pipeline is implemented to combine, define, and compare datasets, and customize their outputs. In contrast to other automated programs, AGA enables flexible selection of sample groups for comparison from complex sample annotations. Batch correction techniques are also included to further enable the combination of datasets from diverse studies in this comparison. AGA also allows users to save plots, tables and data, and log files containing key portions of the R script run for reproducible analyses. The link between the interface and R supports collaborative research, enabling advanced R users to extend preliminary analyses generated from bioinformatics novices.

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Lau, Jessica W., Erik Lehnert, Anurag Sethi, et al. "The Cancer Genomics Cloud: Collaborative, Reproducible, and Democratized—A New Paradigm in Large-Scale Computational Research." Cancer Research 77, no. 21 (2017): e3-e6. http://dx.doi.org/10.1158/0008-5472.can-17-0387.

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Dissertations / Theses on the topic "Collaborative Reproducible Research"

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Borke, Lukas. "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA." Doctoral thesis, Humboldt-Universität zu Berlin, 2017. http://dx.doi.org/10.18452/18307.

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Mit der wachsenden Popularität von GitHub, dem größten Online-Anbieter von Programm-Quellcode und der größten Kollaborationsplattform der Welt, hat es sich zu einer Big-Data-Ressource entfaltet, die eine Vielfalt von Open-Source-Repositorien (OSR) anbietet. Gegenwärtig gibt es auf GitHub mehr als eine Million Organisationen, darunter solche wie Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly und viele mehr. GitHub verfügt über eine umfassende REST API, die es Forschern ermöglicht, wertvolle Informationen über die Entwicklungszyklen von Software und Forschung abzurufen. Unsere Arbeit verfolgt zwei Hauptziele: (I) ein automatisches OSR-Kategorisierungssystem für Data Science Teams und Softwareentwickler zu ermöglichen, das Entdeckbarkeit, Technologietransfer und Koexistenz fördert. (II) Visuelle Daten-Exploration und thematisch strukturierte Navigation innerhalb von GitHub-Organisationen für reproduzierbare Kooperationsforschung und Web-Applikationen zu etablieren. Um Mehrwert aus Big Data zu generieren, ist die Speicherung und Verarbeitung der Datensemantik und Metadaten essenziell. Ferner ist die Wahl eines geeigneten Text Mining (TM) Modells von Bedeutung. Die dynamische Kalibrierung der Metadaten-Konfigurationen, TM Modelle (VSM, GVSM, LSA), Clustering-Methoden und Clustering-Qualitätsindizes wird als "Smart Clusterization" abgekürzt. Data-Driven Documents (D3) und Three.js (3D) sind JavaScript-Bibliotheken, um dynamische, interaktive Datenvisualisierung zu erzeugen. Beide Techniken erlauben Visuelles Data Mining (VDM) in Webbrowsern, und werden als D3-3D abgekürzt. Latent Semantic Analysis (LSA) misst semantische Information durch Kontingenzanalyse des Textkorpus. Ihre Eigenschaften und Anwendbarkeit für Big-Data-Analytik werden demonstriert. "Smart clusterization", kombiniert mit den dynamischen VDM-Möglichkeiten von D3-3D, wird unter dem Begriff "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA" zusammengefasst.<br>With the growing popularity of GitHub, the largest host of source code and collaboration platform in the world, it has evolved to a Big Data resource offering a variety of Open Source repositories (OSR). At present, there are more than one million organizations on GitHub, among them Google, Facebook, Twitter, Yahoo, CRAN, RStudio, D3, Plotly and many more. GitHub provides an extensive REST API, which enables scientists to retrieve valuable information about the software and research development life cycles. Our research pursues two main objectives: (I) provide an automatic OSR categorization system for data science teams and software developers promoting discoverability, technology transfer and coexistence; (II) establish visual data exploration and topic driven navigation of GitHub organizations for collaborative reproducible research and web deployment. To transform Big Data into value, in other words into Smart Data, storing and processing of the data semantics and metadata is essential. Further, the choice of an adequate text mining (TM) model is important. The dynamic calibration of metadata configurations, TM models (VSM, GVSM, LSA), clustering methods and clustering quality indices will be shortened as "smart clusterization". Data-Driven Documents (D3) and Three.js (3D) are JavaScript libraries for producing dynamic, interactive data visualizations, featuring hardware acceleration for rendering complex 2D or 3D computer animations of large data sets. Both techniques enable visual data mining (VDM) in web browsers, and will be abbreviated as D3-3D. Latent Semantic Analysis (LSA) measures semantic information through co-occurrence analysis in the text corpus. Its properties and applicability for Big Data analytics will be demonstrated. "Smart clusterization" combined with the dynamic VDM capabilities of D3-3D will be summarized under the term "Dynamic Clustering and Visualization of Smart Data via D3-3D-LSA".

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Books on the topic "Collaborative Reproducible Research"

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Bausell, R. Barker. The Problem with Science. Oxford University Press, 2021. http://dx.doi.org/10.1093/oso/9780197536537.001.0001.

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This book tells the story of how a cadre of dedicated, iconoclastic scientists raised the awareness of a long-recognized preference for publishing positive, eye-catching, but irreproducible results to the status of a genuine scientific crisis. Most famously encapsulated in 2005 by John Ioannidis’s iconic title, “Why Most Published Research Findings Are False,” awareness of the seriousness of the crisis itself was in full bloom sometime around 2011–2012, when a veritable flood of supporting empirical and methodological work began appearing in the scientific literature detailing both the extent of the crisis and how it could be ameliorated. Perhaps most importantly were a number of mass replications of large sets of published psychology experiments (100 in all) by the Open Science Collaboration, preclinical cancer experiments (53) that a large pharmaceutical company considered sufficiently promising to pursue if the original results were reproducible, and 67 similarly promising studies upon which an even larger pharmaceutical company decided to replicate prior to initiating the expense and time-consuming developmental process. Shockingly, less than 50% of these 220 study results could be replicated, thereby providing unwelcomed evidence that John Ioannidis’s projections (and others performed both earlier and later) that more than half of published scientific results were false and could not be reproduced by other scientists. Fortunately, a plethora of practical, procedural behaviors accompanied these demonstrations and projects that were quite capable of greatly reducing the prevalence of future irreproducible results. Therefore the primary purpose of this book is use these impressive labors of hundreds of methodologically oriented scientists to provide guidance to practicing and aspiring scientists regarding how (a) to change the way in which science has historically been both conducted and reported in order to avoid producing false-positive, irreproducible results in their own work and, (b) ultimately, to change those institutional practices (primarily but not exclusively involving the traditional journal publishing process and the academic reward system) that have unwittingly contributed to the present crisis. For what is actually needed is nothing less than a change in the scientific culture itself to one that will prioritize conducting research correctly in order to get things right rather than simply to get published. Hopefully this book can make a small contribution to that end.

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Book chapters on the topic "Collaborative Reproducible Research"

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Gandrud, Christopher. "Storing, Collaborating, Accessing Files, and Versioning." In Reproducible Research with R and RStudio. Chapman and Hall/CRC, 2020. http://dx.doi.org/10.1201/9780429031854-7.

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"The Reproducibility Project: A Model of Large-Scale Collaboration for Empirical Research on Reproducibility." In Implementing Reproducible Research. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315373461-11.

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Gandrud, Christopher. "Storing, Collaborating, Accessing Files, and Versioning." In Reproducible Research with R and RStudio. Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/9781315382548-5.

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Conference papers on the topic "Collaborative Reproducible Research"

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Aiyetan, Paul, Paul Donovan, David Mott, et al. "Abstract 2489: Enabling data access, sharing, collaborative and reproducible research: The Frederick National Laboratory for Cancer Research (FNLCR) data coordinating center." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.am2019-2489.

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Aiyetan, Paul, Paul Donovan, David Mott, et al. "Abstract 2489: Enabling data access, sharing, collaborative and reproducible research: The Frederick National Laboratory for Cancer Research (FNLCR) data coordinating center." In Proceedings: AACR Annual Meeting 2019; March 29-April 3, 2019; Atlanta, GA. American Association for Cancer Research, 2019. http://dx.doi.org/10.1158/1538-7445.sabcs18-2489.

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Coffey, Aoife, Louise Burgoyne, and Brendan Palmer. "Digital Badge in the Responsible Conduct of Research." In Learning Connections 2019: Spaces, People, Practice. University College Cork||National Forum for the Enhancement of Teaching and Learning in Higher Education, 2019. http://dx.doi.org/10.33178/lc.2019.03.

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University College Cork is committed to the highest standard of Research Integrity (RI). The recently published National Framework on the Transition to an Open Research Environment aims to move Ireland another step closer to an open research environment (National Open Research Forum, 2019). One of the central elements underpinning the framework is Research Integrity and Responsible Research practice. This is also reflective of the international emphasis on not only a more open research environment but on more transparent and robust research practices generally, with a particular focus on data management and availability ( Wilkinson et al., 2016). In 2016 a Research Integrity Pilot was run in the UCC Skills Centre in collaboration with the Office of the Vice President for Research and Innovation (OVPRI) and interested academics from the UCC community. Working closely with the Dean of Graduate studies, this pilot resulted in the development of the module PG6015 An Introduction to Research Integrity, Ethics and Open Science for postgraduate students. The new module did not address the needs of staff however, who needed an offering that was more condensed, targeted yet flexible when required. Along this developmental journey, UCC consulted with some leading experts in the field of Research Integrity (RI) by hosting, Prof. Philip DeShong and Prof. Robert Dooling from the University of Maryland via a Fulbright Specialist Award. This award facilitated real insight and a fuller understanding of what RI means together with the need for discipline specific discussion and debate around the topic of Responsible Conduct in Research in its fullest sense. In 2018, access to the Epigeum online course in Research Integrity was enabled through the National Research Integrity Forum. This course provides a good basis for learning in the area of RI but it does not address a need for a blended learning approach around the topics of Responsible Conduct of Research. Through this process began the genesis of an idea which in 2019 resulted in the development of the UCC Digital Badge in the Responsible Conduct of Research. Micro-credentials are a new and innovative learning platform that rewards learner effort outside of traditional pathways, digital badges are an example of these. The Digital Badge in the Responsible Conduct of Research is a research led, team based initiative developed through a unique interdisciplinary collaboration between central research services at UCC. The collaborative process has resulted in an offering that gives an integrated and comprehensive view of three distinct but related areas, Research Integrity, Research Data Management & the Fair Principles and Reproducible Research. Developed by OVPRI, UCC Library and the Clinical Research Facility-Cork (CRF-C), each of the collaborators were already providing training and resources in there own niche but realised a more holistic approach would be greater than the sum of its parts. The purpose of the Digital Badge is to foster and embed best practice and the key elements of Responsible Research in the UCC research community. It offers researchers an opportunity to address significant gaps in their skills and prepares them for the changes in the research landscape occurring both nationally and internationally.

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Jansen, Christoph, Maximilian Beier, Michael Witt, Sonja Frey, and Dagmar Krefting. "Towards Reproducible Research in a Biomedical Collaboration Platform following the FAIR Guiding Principles." In UCC '17: 10th International Conference on Utility and Cloud Computing. ACM, 2017. http://dx.doi.org/10.1145/3147234.3148104.

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