Distribution of sociodemographic and pandemic-related factors at Experiment I (T0) and Experiment II. by Bruno Kluwe-Schiavon (10004763)

Pearson correlation on psychological symptoms, personality traits and emotional regulation skills at T0. by Bruno Kluwe-Schiavon (10004763)

Equivalences between Oxford stringency measures and scale 0 to 1. by Miguel González-Leonardo (13910610)

Viscous fingering at 0.5 s under different interfacial tensions. by Yu-Ting Wu (2057263)

Velocity distribution at 0.5s under different surface tensions. by Yu-Ting Wu (2057263)

Shares of specialist groups prescribing 1^st, 2^nd, and 3^rd antibiotic prescriptions in UTI treatment of females (N39.0, N30.0) in 2018 and 2019. by Thomas Hanslmeier (19949694)

Result from pathway analysis by metaboanalyst5.0. by Jie Zeng (315334)

Descriptive characteristics of the two treatment groups at the inclusion (D0). by Luccheri Ndong Akomezoghe (18585684)

Forests to Faucets 2.0 by U.S. Forest Service (17476914)

“…As developed in Forests to Faucets (USFS 2011), the Important Areas for Surface Drinking Water (IMP) model can be broken down into two parts: IMPn = (PRn) * (Qn)Calculated using R, Updated September 2023IMP_RIMP, Important Areas for Surface Drinking Water (0-100 Quantiles)Calculated using R, Updated September 2023NON_FORESTAcres of non-forestPADUS and NLCDPRIVATE_FORESTAcres of private forestPADUS and NLCDPROTECTED_FORESTAcres of protected forest (State, Local, NGO, Permanent Easement)PADUS, NCED, and NLCDNFS_FORESTAcres of National Forest System (NFS) forestPADUS and NLCDFEDERAL_FORESTAcres of Other Federal forest (Non-NFS Federal)PADUS and NLCDPER_FORPRIPercent Private ForestCalculated using ArcGISPER_FORNFSPercent NFS ForestCalculated using ArcGISPER_FORPROPercent Protected (Other State, Local, NGO, Permanent Easement, NFS, and Federal) ForestCalculated using ArcGISWFP_HI_ACAcres with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_WFPPercent of HU 12 with High and Very High Wildfire Hazard Potential (WHP)Dillon, 2018PER_IDRISKPercent of HU 12 that is at risk for mortality - 25% of standing live basal area greater than one inch in diameter will die over a 15- year time frame (2013 to 2027) due to insects and diseases.Krist, et Al,. 2014PERDEV_1040_45% Landuse Change 2010-2040 (low)ICLUSPERDEV_1090_45% Landuse Change 2010-2090 (low)ICLUSPERDEV_1040_85% Landuse Change 2010-2040 (high)ICLUSPERDEV_1090_85% Landuse Change 2010-2090 (high)ICLUSPER_Q40_45% Water Yield Change 2010-2040 (low) WASSI , Updated September 2023PER_Q90_45% Water Yield Change 2010-2090 (low) WASSI , Updated September 2023PER_Q40_85% Water Yield Change 2010-2040 (high) WASSI , Updated September 2023PER_Q90_85% Water Yield Change 2010-2090 (high) WASSI , Updated September 2023WFP(APCW_R * IMP_R * PER_WFP )/ 10,000Wildfire Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023IDRISK(APCW_R * IMP_R * PER_IDRISK )/ 10,000Insect & Disease Threat to Important Surface Drinking Water Watersheds Calculated using ArcGIS, Updated September 2023DEV1040_45(APCW_R * IMP_R * PERDEV_1040_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_45(APCW_R * IMP_R * PERDEV_1090_45)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023DEV1040_85(APCW_R * IMP_R * PERDEV_1040_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023DEV1090_85(APCW_R * IMP_R * PERDEV_1090_85)/ 10,000 Landuse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_45-1 * (APCW_R * IMP_R * PER_Q40_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_45-1 * (APCW_R * IMP_R * PER_Q90_45)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) Calculated using ArcGIS, Updated September 2023Q1040_85-1 * (APCW_R * IMP_R * PER_Q40_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) Calculated using ArcGIS, Updated September 2023Q1090_85-1 * (APCW_R * IMP_R * PER_Q90_85)/ 10,000 Water Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) Calculated using ArcGIS, Updated September 2023WFP_IMP_RWildfire Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023IDRISK_RInsect & Disease Threat to Important Surface Drinking Water Watersheds (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV40_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_45_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023DEV90_85_RLanduse Change in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q40_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2040 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_45_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (low emissions) (0-100 Quantiles)Calculated using R, Updated September 2023Q90_85_RWater Yield Decrease in Important Surface Drinking Water Watersheds 2010-2090 (high emissions) (0-100 Quantiles)Calculated using R, Updated September 2023RegionUS Forest Service Region numberUSFSRegionnameUS Forest Service Region nameUSFSHUC_Num_DiffThis field compares the value in column HUC12(circa 2019 wbd) with the value in HUC_12 (circa 2009 wassi)-1 = No equivalent WASSI HUC. …”

Summary of trend results based on Mann-Kendall test and Sen’s slope estimator for the 34 weather stations (arranged according to administrative division, in bold) for the wet season (May to October), (<i>p</i> < 0.10 = *, <i>p</i> < 0.05 = **, <i>p</i> < 0.01 = ***,—sign indicates decreasing trend). by Mohammed Mainuddin (8723352)

“…<p>Summary of trend results based on Mann-Kendall test and Sen’s slope estimator for the 34 weather stations (arranged according to administrative division, in bold) for the wet season (May to October), (<i>p</i> < 0.10 = *, <i>p</i> < 0.05 = **, <i>p</i> < 0.01 = ***,—sign indicates decreasing trend).…”

Schematic drawing of stimuli design for interval ratio <i>r</i> = 0.5. by Jun Nitta (3877651)

Reliability (mean ICC, lower and upper ICC 95% confidence interval) and percent difference (PD) for subcortical grey matter volumes, separated by hemisphere (T<0 reflects Skyra>Ver... by Evelyn Medawar (9774392)

The capacitive retention for 5000 cycles of GCD for the Gel/0.05% MWCNT nanocomposite. by Rabeya Binta Alam (17364707)

Inhibition of Wnt signaling pathway or absence of TSP1 decreases cellular <i>T</i>. <i>cruzi</i> infection. by Ashutosh Arun (2574559)

Elemental distribution of FG0. by Qingwei Sun (11457516)

“…Furthermore, porosity and the average pore size decreased and the proportion of small holes in the pores gradually increased. …”

ROB 2.0 for RCT. by Siyuan Chen (200219)

“…The Cochrane Risk of Bias tool 2.0 and the Risk of Bias In Non-randomized Studies of Interventions tool were used for the quality assessment. …”

Number of participants with retinol plasma concentrations in the vitamin A deficiency (<0.7μM) or insufficiency (0.7–1.05 μM) category. by Hyunyoung Jeong (4385848)

Shares of antibiotic substances on total prescription by quarter from 2013 to 2019 for female and male patients with UTI (N39.0, N30.0)(female: n = 1.681.255 prescriptions; male: n... by Thomas Hanslmeier (19949694)

Information distortion of carbon trading investors’ <i>ε</i> reference <i>p</i>₊(<i>τ</i>, 0), <i>p</i>₀(<i>τ</i>, 0) and <i>p</i>₋(<i>τ</i>, 0) un... by Peng-Cheng Zhang (2453158)

Subjects:

Pretreatment with commercial honey AuNPs decreased levels of phospho-IκB compared to the LPS-only control (p < 0.001). by John Benjamin W. Duncan (16955227)