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Investigators in the cognitive neurosciences have turned to Big Data to address persistent replication and reliability issues by increasing sample sizes, statistical power, and representativeness of data. While there is tremendous potential to advance science through open data sharing, these efforts unveil a host of new questions about how to integrate data arising from distinct sources and instruments. We focus on the most frequently assessed area of cognition - memory testing - and demonstrate a process for reliable data harmonization across three common measures. We aggregated raw data from 53 studies from around the world which measured at least one of three distinct verbal learning tasks, totaling N = 10,505 healthy and brain-injured individuals. A mega analysis was conducted using empirical bayes harmonization to isolate and remove site effects, followed by linear models which adjusted for common covariates. After corrections, a continuous item response theory (IRT) model estimated each individual subject’s latent verbal learning ability while accounting for item difficulties. Harmonization significantly reduced inter-site variance by 37% while preserving covariate effects. The effects of age, sex, and education on scores were found to be highly consistent across memory tests. IRT methods for equating scores across AVLTs agreed with held-out data of dually-administered tests, and these tools are made available for free online. This work demonstrates that large-scale data sharing and harmonization initiatives can offer opportunities to address reproducibility and integration challenges across the behavioral sciences.
Non-standard errors
(2021)
In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in sample estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: non-standard errors. To study them, we let 164 teams test six hypotheses on the same sample. We find that non-standard errors are sizeable, on par with standard errors. Their size (i) co-varies only weakly with team merits, reproducibility, or peer rating, (ii) declines significantly after peer-feedback, and (iii) is underestimated by participants.
Razina pročišćenosti alogenoga koštanog bloka = Variant purification of an allogeneic bone block
(2017)
Svrha: Ovaj kratak tekst izvještava o histološkoj analizi sastava komercijalno raspoloživih alogenih koštanih blokova Maxgraft®. Materijali i metode: Na temelju objavljenih histoloških metoda prazni uzorci alogenih koštanih blokova Maxgraft® dekalcificirani su, dehidrirani i uloženi u parafin prije histološkog i histokemijskog bojenja. Nakon toga na prerezima su se procjenjivala obilježja materijala, poput strukture koštanoga matriksa i druge komponente, uključujući kolagen ili stanice/stanične ostatke. Rezultati: Uočeno je da ovi koštani blokovi imaju trabekularnu strukturu s lamelarnom podorganizacijom. Dodatno su nađeni i stanični ostatci unutar lakuna osteocita i na vanjskim površinama trabekula zajedno s ostatcima intertrabekularnog masnog i vezivnog tkiva, te kolagene strukture, vezivno-tkivne stanice i stanični ostatci. Zaključak: U skladu s dosadašnjim istraživanjima, podatci iz ovoga teksta pokazuju da neke od certificiranih tehnika pročišćavanja ne omogućuju proizvodnju alogenog materijala bez organskih stanica i tkivnih komponenata.