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Replication and the reported crises impacting many fields of research have become a focal point for the sciences. This has led to reforms in publishing, methodological design and reporting, and increased numbers of experimental replications coordinated across many laboratories. While replication is rightly considered an indispensable tool of science, financial resources and researchers time are quite limited. In this perspective, we examine different values and attitudes that scientists can consider when deciding whether to replicate a finding and how. We offer a conceptual framework for assessing the usefulness of various replication tools, such as preregistration.

Vegetation across northwest Europe responded instantly and synchronously to abrupt cooling during the last deglaciation, according to a compilation of precisely dated terrestrial records.

Science is justly admired as a cumulative process (“standing on the shoulders of giants”), yet scientific knowledge is typically built on a patchwork of research contributions without much coordination. This lack of efficiency has specifically been addressed in clinical research by recommendations for living systematic reviews and against research waste. We propose to further those recommendations with ALL-IN meta-analysis: Anytime Live and Leading INterim meta-analysis. ALL-IN provides statistical methodology for a meta-analysis that can be updated at any time—reanalyzing after each new observation while retaining type-I error guarantees, live—no need to prespecify the looks, and leading—in the decisions on whether individual studies should be initiated, stopped or expanded, the meta-analysis can be the leading source of information. We illustrate the method for time-to-event data, showing how synthesizing data at interim stages of studies can increase efficiency when studies are slow in themselves to provide the necessary number of events for completion. The meta-analysis can be performed on interim data, but does not have to. The analysis design requires no information about the number of patients in trials or the number of trials eventually included. So it can breathe life into living systematic reviews, through better and simpler statistics, efficiency, collaboration and communication

The necessity of characterizing snow through objective, physically motivated parameters has led to new model formulations and new measurement techniques. Consequently, essential structural parameters such as density and specific surface area (for basic characterization) or mechanical parameters such as the critical crack length (for avalanche stability characterization) gradually replace the semiempirical indices acquired from traditional stratigraphy. These advances come along with new demands and potentials for validation. To this end, we conducted the RHOSSA field campaign, in reference to density (ρ) and specific surface area (SSA), at the Weissfluhjoch research site in the Swiss Alps to provide a multi-instrument, multi-resolution dataset of density, SSA and critical crack length over the complete winter season of 2015–2016. In this paper, we present the design of the campaign and a basic analysis of the measurements alongside predictions from the model SNOWPACK. To bridge between traditional and new methods, the campaign comprises traditional profiles, density cutter, IceCube, SnowMicroPen (SMP), micro-computed-tomography, propagation saw tests and compression tests. To bridge between different temporal resolutions, the traditional weekly to biweekly (every 2 weeks, used in this sense throughout the paper) snow pits were complemented by daily SMP measurements. From the latter, we derived a recalibration of the statistical retrieval of density and SSA for SMP version 4 that yields an unprecedented spatiotemporal picture of the seasonal evolution of density and SSA in a snowpack. Finally, we provide an intercomparison of measured and modeled estimates of density and SSA for four characteristic layers over the entire season to demonstrate the potential of high-temporal-resolution monitoring for snowpack model validation.

Ground-based surveys of three coal fires and airborne surveys of two of the fires were conducted near Sheridan, Wyoming. The fires occur in natural outcrops and in abandoned mines, all containing Paleocene-age subbituminous coals. Diffuse (carbon dioxide (CO(2)) only) and vent (CO(2), carbon monoxide (CO), methane, hydrogen sulfide (H(2)S), and elemental mercury) emission estimates were made for each of the fires. Additionally, gas samples were collected for volatile organic compound (VOC) analysis and showed a large range in variation between vents. The fires produce locally dangerous levels of CO, CO(2), H(2)S, and benzene, among other gases. At one fire in an abandoned coal mine, trends in gas and tar composition followed a change in topography. Total CO(2) fluxes for the fires from airborne, ground-based, and rate of fire advancement estimates ranged from 0.9 to 780mg/s/m(2) and are comparable to other coal fires worldwide. Samples of tar and coal-fire minerals collected from the mouth of vents provided insight into the behavior and formation of the coal fires.

Studies accumulate over time and meta-analyses are mainly retrospective. These two characteristics introduce dependencies between the analysis time, at which a series of studies is up for meta-analysis, and results within the series. Dependencies introduce bias — Accumulation Bias — and invalidate the sampling distribution assumed for p-value tests, thus inflating type-I errors. But dependencies are also inevitable, since for science to accumulate efficiently, new research needs to be informed by past results. Here, we investigate various ways in which time influences error control in meta-analysis testing. We introduce an Accumulation Bias Framework that allows us to model a wide variety of practically occurring dependencies including study series accumulation, meta-analysis timing, and approaches to multiple testing in living systematic reviews. The strength of this framework is that it shows how all dependencies affect p-value-based tests in a similar manner. This leads to two main conclusions. First, Accumulation Bias is inevitable, and even if it can be approximated and accounted for, no valid p-value tests can be constructed. Second, tests based on likelihood ratios withstand Accumulation Bias: they provide bounds on error probabilities that remain valid despite the bias. We leave the reader with a choice between two proposals to consider time in error control: either treat individual (primary) studies and meta-analyses as two separate worlds — each with their own timing — or integrate individual studies in the meta-analysis world. Taking up likelihood ratios in either approach allows for valid tests that relate well to the accumulating nature of scientific knowledge. Likelihood ratios can be interpreted as betting profits, earned in previous studies and invested in new ones, while the meta-analyst is allowed to cash out at any time and advice against future studies.

The elevated deposition of atmospheric mercury over the southeastern United States is currently not well understood. Here we measure partial columns and vertical profiles of bromine monoxide (BrO) radicals, a key component of mercury oxidation chemistry, to better understand the processes and altitudes at which mercury is being oxidized in the atmosphere. We use data from a ground-based MAX-DOAS instrument located at a coastal site ∼ 1 km from the Gulf of Mexico in Gulf Breeze, FL, where we had previously detected tropospheric BrO (Coburn et al., 2011). Our profile retrieval assimilates information about stratospheric BrO from the WACCM chemical transport model (CTM), and uses only measurements at moderately low solar zenith angles (SZAs) to estimate the BrO slant column density contained in the reference spectrum (SCDRef). The approach has 2.6 degrees of freedom, and avoids spectroscopic complications that arise at high SZA; knowledge about SCDRef further helps to maximize sensitivity in the free troposphere (FT). A cloud-free case study day with low aerosol load (9 April 2010) provided optimal conditions for distinguishing marine boundary layer (MBL: 0–1 km) and free-tropospheric (FT: 1–15 km) BrO from the ground. The average daytime tropospheric BrO vertical column density (VCD) of ∼ 2.3 × 1013 molec cm−2 (SZA 5 molec cm−2 s−1 for bromine, while the contribution from ozone (O3) is 0.8 × 105 molec cm−2 s−1. Chlorine-induced oxidation is estimated to add 2), and to a lesser extent also HO2 radicals. Using a 3-D CTM, we find that surface GOM variations are also typical of other days, and are mainly derived from the FT. Bromine chemistry is active in the FT over Gulf Breeze, where it forms water-soluble GOM that is subsequently available for wet scavenging by thunderstorms or transport to the boundary layer.

Programmable Thermal Dissociation (PTD) has been used to investigate the chemical speciation of Reactive Gaseous Mercury (RGM, Hg2+). RGM was collected on denuders and analyzed using PTD. The technique was tested in a field campaign at a coal-fired power plant in Pensacola, Florida. Stack gas samples were collected from ducts located after the electrostatic precipitator and prior to entering the stack. An airship was used to sample from the stack plume, downwind of the stack exit. The PTD profiles from these samples were compared with PTD profiles of HgCl2. Comparison of stack and in-plume samples suggest that the chemical speciation are the same and that it is possible to track a specific chemical form of RGM from the stack and follow its evolution in the stack plume. Comparison of the measured plume RGM with the amount calculated from in-stack measurements and the measured plume dilution suggest that the stack and plume RGM concentrations are consistent with dilution. The PTD profiles of the stack and plume samples are consistent with HgCl2 being the chemical form of the sampled RGM. Comparison with literature PTD profiles of reference mercury compounds suggests no other likely candidates for the speciation of RGM.