YODA: YODA's Organigram on Data Analysis
Creators: name: Hanke, Michael
name: Meyer, Kyle A.
name: Castello, Matteo Visconti Di Oleggio
name: Poldrack, Benjamin
name: Halchenko, Yaroslav O.
Publisher: name: F1000 Research Limited
Year: 2018

description: Although "incremental progress" is often referred to as the lesser kind of progress, it is arguably the true foundation of the scientific process. At the same time, building atop one's own or other's achievements of the past is frequently a hard problem. Data formats, computational tools and environments, best scientific practices, and means of exchange of data and results are continuously changing. This makes it challenging to effectively re-use previous scientific artefacts, beyond plain theoretical advances in a field.<br><br>A core factor behind these issues is the inability of established tools and workflows for managing and  conducting data analyses to adequately capture and verify the complete set of assumptions and preconditions that represent the conceptual basis of any analysis. As a consequence, previous results frequently fail to recompute because "it doesn't run anymore", previous analysis implementations do not work with new data because "something is wrong with these data", or, worse, results are unexpected or plain wrong because "these parts are copied from over here, I think it needs them".<br><br>Well established free and open source tools our approach relies on: Git, git-annex, and DataLad. This combination of tools enables the reliable, non-ambiguous tracking of different components of a study (e.g., inputs, code, output), while facilitating their independent re-use (e.g., the same data used across multiple studies; versions of a software library used repeatedly), and scales to the dataset sizes found in cutting edge high-resolution neuroimaging research.<br><br>We propose a simple and intuitive organization of digital components of a scientific study. This comprises: 1) structured (raw) dataset following community standards (BIDS); 2) custom analysis and re-usable code components; 3) complete execution environments (Docker/Singularity); 3) documents for human consumption (notes, article manuscripts); 4) essential metadata of a study (authorship, change log, funding, etc.); 5) programmatic tests to verify the completeness and integrity of a study (e.g. acquisition protocol compliance checks); 6) software adaptors for automated computing, such as continuous integration systems, or high-performance computing environments.<br><br>DataLad provides a high-level interface that enables users to create and manage these components. Additionally, it supports provenance capture of performed analyses steps ("datalad run"), and easy verification that an update of a component (e.g., an input dataset, or an entire computational environment image) results in reproducible results.<br><br>We demonstrate how the proposed organization can be used for common research tasks, ranging from capturing acquired data, to the publication of a journal article. This includes a study template  https://github.com/myyoda/template), and concrete demos based on published articles as well as unpublished analyses (e.g. https://github.com/ReproNim/simple_workflow, https://github.com/kyleam/mlb-rundiff) that illustrate how studies can be comprehensively expressed in a portable and reproducible manner.<br><br>We outline a simple approach to structuring and conducting data analyses that aims to tightly connect all their essential ingredients: data, code, and computational environments in a transparent, modular, accountable, and practical way. It exclusively relies on a file naming convention and tools that are already available to anyone, and is built on workflows that have been proven to be capable of effective large scale collaboration. These tools not only do improve accountability, collaboration, andreproducibility of the research, but also facilitate novel workflows for experimentation and efficient use of available free online platforms and services.<br><br>Acknowledgements<br><br>This work was supported by the CRCNS (BMBF 01GQ1411; NSF 1429999, NIH (#1P41EB019936-01A1), and the European Regional Development Fund (ERDF), Project: Center for Behavioral Brain Sciences.