1. |
- Gorgolewski, Krzysztof J., et al.
(author)
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BIDS apps: Improving ease of use, accessibility, and reproducibility of neuroimaging data analysis methods
- 2017
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In: PloS Computational Biology. - : Public Library of Science (PLoS). - 1553-734X .- 1553-7358. ; 13:3
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Journal article (peer-reviewed)abstract
- The rate of progress in human neurosciences is limited by the inability to easily apply a wide range of analysis methods to the plethora of different datasets acquired in labs around the world. In this work, we introduce a framework for creating, testing, versioning and archiving portable applications for analyzing neuroimaging data organized and described in compliance with the Brain Imaging Data Structure (BIDS). The portability of these applications (BIDS Apps) is achieved by using container technologies that encapsulate all binary and other dependencies in one convenient package. BIDS Apps run on all three major operating systems with no need for complex setup and configuration and thanks to the comprehensiveness of the BIDS standard they require little manual user input. Previous containerized data processing solutions were limited to single user environments and not compatible with most multi-tenant High Performance Computing systems. BIDS Apps overcome this limitation by taking advantage of the Singularity container technology. As a proof of concept, this work is accompanied by 22 ready to use BIDS Apps, packaging a diverse set of commonly used neuroimaging algorithms.
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2. |
- Lakens, Daniel, et al.
(author)
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Justify your alpha
- 2018
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In: Nature Human Behaviour. - : Nature Publishing Group. - 2397-3374. ; 2:3, s. 168-171
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Journal article (peer-reviewed)abstract
- In response to recommendations to redefine statistical significance to P ≤ 0.005, we propose that researchers should transparently report and justify all choices they make when designing a study, including the alpha level.
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3. |
- Nissenbaum, J, et al.
(author)
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Susceptibility to chronic pain following nerve injury is genetically affected by CACNG2
- 2010
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In: Genome research. - : Cold Spring Harbor Laboratory. - 1549-5469 .- 1088-9051. ; 20:9, s. 1180-1190
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Journal article (peer-reviewed)abstract
- Chronic neuropathic pain is affected by specifics of the precipitating neural pathology, psychosocial factors, and by genetic predisposition. Little is known about the identity of predisposing genes. Using an integrative approach, we discovered that CACNG2 significantly affects susceptibility to chronic pain following nerve injury. CACNG2 encodes for stargazin, a protein intimately involved in the trafficking of glutamatergic AMPA receptors. The protein might also be a Ca2+ channel subunit. CACNG2 has previously been implicated in epilepsy. Initially, using two fine-mapping strategies in a mouse model (recombinant progeny testing [RPT] and recombinant inbred segregation test [RIST]), we mapped a pain-related quantitative trait locus (QTL) (Pain1) into a 4.2-Mb interval on chromosome 15. This interval includes 155 genes. Subsequently, bioinformatics and whole-genome microarray expression analysis were used to narrow the list of candidates and ultimately to pinpoint Cacng2 as a likely candidate. Analysis of stargazer mice, a Cacng2 hypomorphic mutant, provided electrophysiological and behavioral evidence for the gene's functional role in pain processing. Finally, we showed that human CACNG2 polymorphisms are associated with chronic pain in a cohort of cancer patients who underwent breast surgery. Our findings provide novel information on the genetic basis of neuropathic pain and new insights into pain physiology that may ultimately enable better treatments.
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4. |
- Rohrer, Julia M., et al.
(author)
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Putting the Self in Self-Correction : Findings From the Loss-of-Confidence Project
- 2021
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In: Perspectives on Psychological Science. - : Sage Publications. - 1745-6916 .- 1745-6924. ; 16:6, s. 1255-1269
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Journal article (peer-reviewed)abstract
- Science is often perceived to be a self-correcting enterprise. In principle, the assessment of scientific claims is supposed to proceed in a cumulative fashion, with the reigning theories of the day progressively approximating truth more accurately over time. In practice, however, cumulative self-correction tends to proceed less efficiently than one might naively suppose. Far from evaluating new evidence dispassionately and infallibly, individual scientists often cling stubbornly to prior findings. Here we explore the dynamics of scientific self-correction at an individual rather than collective level. In 13 written statements, researchers from diverse branches of psychology share why and how they have lost confidence in one of their own published findings. We qualitatively characterize these disclosures and explore their implications. A cross-disciplinary survey suggests that such loss-of-confidence sentiments are surprisingly common among members of the broader scientific population yet rarely become part of the public record. We argue that removing barriers to self-correction at the individual level is imperative if the scientific community as a whole is to achieve the ideal of efficient self-correction.
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5. |
- Schweinsberg, Martin, et al.
(author)
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Same data, different conclusions : Radical dispersion in empirical results when independent analysts operationalize and test the same hypothesis
- 2021
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In: Organizational Behavior and Human Decision Processes. - : Elsevier BV. - 0749-5978 .- 1095-9920. ; 165, s. 228-249
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Journal article (peer-reviewed)abstract
- In this crowdsourced initiative, independent analysts used the same dataset to test two hypotheses regarding the effects of scientists' gender and professional status on verbosity during group meetings. Not only the analytic approach but also the operationalizations of key variables were left unconstrained and up to individual analysts. For instance, analysts could choose to operationalize status as job title, institutional ranking, citation counts, or some combination. To maximize transparency regarding the process by which analytic choices are made, the analysts used a platform we developed called DataExplained to justify both preferred and rejected analytic paths in real time. Analyses lacking sufficient detail, reproducible code, or with statistical errors were excluded, resulting in 29 analyses in the final sample. Researchers reported radically different analyses and dispersed empirical outcomes, in a number of cases obtaining significant effects in opposite directions for the same research question. A Boba multiverse analysis demonstrates that decisions about how to operationalize variables explain variability in outcomes above and beyond statistical choices (e.g., covariates). Subjective researcher decisions play a critical role in driving the reported empirical results, underscoring the need for open data, systematic robustness checks, and transparency regarding both analytic paths taken and not taken. Implications for orga-nizations and leaders, whose decision making relies in part on scientific findings, consulting reports, and internal analyses by data scientists, are discussed.
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