USpaceThis link opens in a new windowUSpace is the University of Utah's open access institutional repository. We currently offer data consultation for datasets of all sizes and archiving services for datasets and related documentation for files up to 40 MB. If USpace is not the right repository for you, our data librarians can help you find an appropriate repository for your data.
ICPSR: Inter-University Consortium for Political and Social ResearchThis link opens in a new windowICPSR maintains a data archive of more than 500,000 files of research in the social sciences. It hosts 16 specialized collections of data in education, aging, criminal justice, substance abuse, terrorism, and other fields.
This database provides access to a collection of sociological and demographic data. The majority of ICPSR data holdings are public-use files with no restrictions on their access. ICPSR provides access to restricted use versions that retain confidential data by imposing stringent requirements for accessing them. The datasets which are available to the University of Utah are included in this collection. Topics covered by this database include criminal justice, health and aging, substance abuse and mental health, child care, and health and medical care.
figsharePublish all your research outputs in seconds in an easily citable, sharable, and discoverable manner.
DryadAn international repository of data underlying peer-reviewed articles in the basic and applied biosciences. Dryad enables scientists to validate published findings, explore new analysis methodologies, repurpose data for research questions unanticipated by the original authors, and perform synthetic studies.
Steven N. Goodman, Daniele Fanelli and John P. A. Ioannidis. Science Translational Medicine 01 Jun 2016: Vol. 8, Issue 341, pp. 341ps12. DOI: 10.1126/scitranslmed.aaf5027
Tsilidis KK, Panagiotou OA, Sena ES, Aretouli E, Evangelou E, Howells DW, et al. (2013) Evaluation of Excess Significance Bias in Animal Studies of Neurological Diseases. PLoS Biol 11(7): e1001609. doi:10.1371/journal.pbio.1001609
Young NS, Ioannidis JPA, Al-Ubaydli O (2008) Why Current Publication Practices May Distort Science. PLoS Med 5(10): e201. doi:10.1371/journal.pmed.0050201
Landis, Story C., et al. "A call for transparent reporting to optimize the predictive value of preclinical research." Nature 490.7419 (2012): 187. doi: 10.1038/nature11556
Initiatives to Follow
Retraction WatchA blog that reports on retractions in scientific literature.
Center for Open ScienceCOS is a non-profit technology company providing free and open services to increase inclusivity and transparency of research. COS supports shifting incentives and practices to align more closely with scientific values.
Reproducibility Project: Cancer BiologyIndependently replicating a subset of experimental results from a number of high-profile papers in the field of cancer biology studies published between 2010-2012 using the Science Exchange network of expert scientific labs.
Curate ScienceCurate Science summarizes independent direct replication results meta-analytically so researchers can calibrate their beliefs in empirical findings according to independent replication evidence.
The NIH CommonsThe Commons is a shared virtual space where scientists can work with the digital objects of biomedical research, i.e. it is a system that will allow investigators to find, manage, share, use and reuse data, software, metadata and workflows.
Researchobjects.orgAims to map the landscape of initiatives and activity in the development of Research Objects, an emerging approach to the publication, and exchange of scholarly information on the Web. Research objects are not just data, not just collections, but any digital resource that aims to go beyond the PDF for scholarly publishing!
ReproZipPack your research along with all necessary data files, libraries, environment variables and options. Then anybody can reproduce the research on a different machine, without tracking down and installing the dependencies, or even having to run the same operating system!
Guidelines & Recommendations
Equator NetworkThe EQUATOR (Enhancing the QUAlity and Transparency Of health Research) Network is an international initiative that seeks to improve the reliability and value of published health research literature by promoting transparent and accurate reporting and wider use of robust reporting guidelines.
NIH Rigor and Reproducibility in Grant ApplicationsThe information provided on this website is designed to assist the extramural community in addressing rigor and transparency in NIH grant applications and progress reports.
Principles and Guidelines for Reporting Preclinical ResearchNIH held a joint workshop in June 2014 with the Nature Publishing Group and Science on the issue of reproducibility and rigor of research findings, with journal editors representing over 30 basic/preclinical science journals in which NIH-funded investigators have most often published. The workshop focused on identifying the common opportunities in the scientific publishing arena to enhance rigor and further support research that is reproducible, robust, and transparent.
Reproducible Research with R and RStudio by Christopher GandrudWith straightforward examples, the book takes you through a reproducible research workflow, showing you how to use: R for dynamic data gathering and automated results presentation; knitr for combining statistical analysis and results into one document; LaTeX for creating PDF articles and slide shows, and Markdown and HTML for presenting results on the web.
Call Number: Marriott Q180.55.S7 G36 2014
ISBN: 9781466572843
The Cult of Statistical Significance by Deirdre Nansen McCloskey; Steve ZiliakThe Cult of Statistical Significance shows, field by field, how "statistical significance," a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ "testing" that doesn't test and "estimating" that doesn't estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots.
Implementing Reproducible Research by
Implementing Reproducible Research covers many of the elements necessary for conducting and distributing reproducible research. It explains how to accurately reproduce a scientific result. Divided into three parts, the book discusses the tools, practices, and dissemination platforms for ensuring reproducibility in computational science.
Reproducible Research with R and RStudio by
With straightforward examples, the book takes you through a reproducible research workflow, showing you how to use: R for dynamic data gathering and automated results presentation; knitr for combining statistical analysis and results into one document; LaTeX for creating PDF articles and slide shows, and Markdown and HTML for presenting results on the web.
The Cult of Statistical Significance by
The Cult of Statistical Significance shows, field by field, how "statistical significance," a technique that dominates many sciences, has been a huge mistake. The authors find that researchers in a broad spectrum of fields, from agronomy to zoology, employ "testing" that doesn't test and "estimating" that doesn't estimate. The facts will startle the outside reader: how could a group of brilliant scientists wander so far from scientific magnitudes? This study will encourage scientists who want to know how to get the statistical sciences back on track and fulfill their quantitative promise. The book shows for the first time how wide the disaster is, and how bad for science, and it traces the problem to its historical, sociological, and philosophical roots.
Bioinformatics Data Skills by
Learn the data skills necessary for turning large sequencing datasets into reproducible and robust biological findings.
