| 1. |
- Menkveld, Albert J., et al.
(author)
-
Nonstandard Errors
- 2024
-
In: JOURNAL OF FINANCE. - : Wiley-Blackwell. - 0022-1082 .- 1540-6261. ; 79:3, s. 2339-2390
-
Journal article (peer-reviewed)abstract
- In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in 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-nonstandard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for more reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants.
|
|
| 2. |
- Boudreau, Mathieu, et al.
(author)
-
Repeat it without me: Crowdsourcing the T1 mapping common ground via the ISMRM reproducibility challenge
- 2024
-
In: MAGNETIC RESONANCE IN MEDICINE. - 0740-3194 .- 1522-2594. ; 92:3, s. 1115-1127
-
Journal article (peer-reviewed)abstract
- Purpose T-1 mapping is a widely used quantitative MRI technique, but its tissue-specific values remain inconsistent across protocols, sites, and vendors. The ISMRM Reproducible Research and Quantitative MR study groups jointly launched a challenge to assess the reproducibility of a well-established inversion-recovery T-1 mapping technique, using acquisition details from a seminal T-1 mapping paper on a standardized phantom and in human brains. Methods The challenge used the acquisition protocol from Barral et al. (2010). Researchers collected T-1 mapping data on the ISMRM/NIST phantom and/or in human brains. Data submission, pipeline development, and analysis were conducted using open-source platforms. Intersubmission and intrasubmission comparisons were performed. Results Eighteen submissions (39 phantom and 56 human datasets) on scanners by three MRI vendors were collected at 3 T (except one, at 0.35 T). The mean coefficient of variation was 6.1% for intersubmission phantom measurements, and 2.9% for intrasubmission measurements. For humans, the intersubmission/intrasubmission coefficient of variation was 5.9/3.2% in the genu and 16/6.9% in the cortex. An interactive dashboard for data visualization was also eveloped: https://rrsg2020.dashboards.neurolibre.org. Conclusion The T-1 intersubmission variability was twice as high as the intrasubmission variability in both phantoms and human brains, indicating that the acquisition details in the original paper were insufficient to reproduce a quantitative MRI protocol. This study reports the inherent uncertainty in T-1 measures across independent research groups, bringing us one step closer to a practical clinical baseline of T-1 variations in vivo.
|
|
| 3. |
- Hu, Zengyun, et al.
(author)
-
“Dry gets drier, wet gets wetter”: A case study over the arid regions of central Asia
- 2019
-
In: International Journal of Climatology. - : Wiley. - 0899-8418 .- 1097-0088. ; 39:2, s. 1072-1091
-
Journal article (peer-reviewed)abstract
- © 2018 Royal Meteorological Society The “dry gets drier, wet gets wetter” (DGDWGW) paradigm well describes the pattern of precipitation changes over the oceans. However, it has also been usually considered as a simplified pattern of regional changes in wet/dry under global warming, although GCMs mostly do not agree this pattern over land. To examine the validity of this paradigm over land and evaluate how usage of drought indices estimated from different hydrological variables affects detection of regional wet/dry trends, we take the arid regions of central Asia as a case study area and estimate the drying and wetting trends during the period of 1950–2015 based on multiple drought indices. These indices include the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), the Palmer drought severity index (PDSI) and self-calibrating PDSI (sc_PDSI) with both the Thornthwaite (th) and Penman–Monteith (pm) equations in PDSI calculation (namely, PDSI_th, PDSI_pm, sc_PDSI_th and sc_PDSI_pm). The results show that there is an overall agreement among the indices in terms of inter-annual variation, especially for the PDSIs. All drought indices except SPI show a drying trend over the five states of central Asia (CAS5: including Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan). The four PDSIs and SPEI reveal a wetting tendency over the northwestern China (NW; including Xinjiang Uygur Autonomous Region and Hexi Corridor). The contrasting trends between CAS5 and NW can also be revealed in soil moisture (SM) variations. The nonlinear wet and dry variations are dominated by the 3–7 years oscillations for the indices. Relationships between the six indices and climate variables show the major drought drivers have regional features: with mean temperature (TMP), precipitation total (PRE) and potential evapotranspiration (PET) for CAS5, and PRE and PET for NW. Finally, our analyses indicate that the dry and wet variations are strongly correlated with the El Niño/Southern Oscillation (ENSO).
|
|
| 4. |
- Ye, Tao, et al.
(author)
-
Reducing livestock snow disaster risk in the Qinghai–Tibetan Plateau due to warming and socioeconomic development
- 2022
-
In: Science of the Total Environment. - : Elsevier BV. - 0048-9697 .- 1879-1026. ; 813
-
Journal article (peer-reviewed)abstract
- Global warming can have positive or negative impacts on society depending on sectors and changes in climate impact drivers, resulting in opportunities or risks. The same holds true for social-economic changes. However, past research has mostly focused on assessing risks, leaving potential opportunities under-addressed. Here, we simulated the impact of climate change and socioeconomic development on livestock snow disasters over the Qinghai–Tibetan Plateau during 1986–2100, by integrating the drivers of climate and socioeconomic changes via an event-based disaster risk assessment model. Model results show climate change and socioeconomic development contributes about equally to reducing livestock loss in snow disasters by 4% yr−1 up to 2100 under representative concentration pathway 8.5 and shared-socioeconomic pathway 5. The marginal benefit from climate change was projected to be a 43.2% reduction in annual average loss per degree kelvin warming, and that from socioeconomic development was a 12.4% reduction per 100% increase in gross domestic production. In a 2 °C warmer world, the annual average loss could be 91% smaller compared with the baseline period (1986–2005). Limiting global warming to 1.5 °C instead of 2 °C would reduce the benefit by 5%, requiring a 135% increase in the marginal benefits of prevention capacity to offset the reduction.
|
|
