Data from: Developing a growing degree day model to guide integrated pest management of <i>Eucosma giganteana, </i>a pest of a novel perennial oilseed crop by HAZEL SCRIBNER (20664341)

“…In the field, each sticky card was enclosed in a chicken wire cage (2in mesh) to reduce capture of birds and other large nontarget species. …”

miR-184 does not downregulate target pSJ and TCJ mRNA levels but changes Nrv2 expression. by Sravya Paluri (5475260)

Multiple Knee Points in the Degradation of a Commercial Lithium-ion Battery: A Case Study of the NCM/Graphite System (Supporting Information) by Yui FUJIHARA (17772971)

“…<div>Lithium-ion batteries are used on an increasingly large scale, making their lifetime prediction a critical issue. …”

Methodology for measurement of the macular topographic changes. by Seung Min Lee (1644409)

Flow chart with steps of conducting the study. by Athira Satheesh Kumar (20570553)

“…It is observed that decreasing the number of individual connections does not reduce the size of the rejector population when there are large numbers of messages sent through groups. …”

Threading Behavior and Dynamics of Ring-Linear Polymer Blends under Poiseuille Flow by Deyin Wang (6028850)

“…When the flow field strength exceeds this critical value, ring-linear polymer blends will aggregate into a cluster due to the combination of entanglement between polymers and the large differences in the velocities of the polymers. …”

AjMLKL is involved in 10⁸ CFU/mL AJ01 induced lytic coelomocyte death independent on its phosphorylation. by Kaiyu Chen (11691918)

Measurement of interference efficiency of AjNLRC4 or AjCASP-1 by specific siRNA transfection. by Kaiyu Chen (11691918)

Preference for the EIA – conjoint results. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Marginal means – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample attribute table. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Subgroup analysis – Political affiliation. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Sample scenario description. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

AMCEs – Pooled across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Methodological flowchart. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Preference for the EIA vs. ETA across scenarios. by Mehdi Mourali (10170245)

“…When are individuals more likely to support equal treatment algorithms (ETAs), characterized by higher predictive accuracy, and when do they prefer equal impact algorithms (EIAs) that reduce performance gaps between groups? A randomized conjoint experiment and a follow-up choice experiment revealed that support for the EIAs decreased sharply as their accuracy gap grew, although impact parity was prioritized more when ETAs produced large outcome discrepancies. …”

Modeling the relationship between size, environmental oxygen, and the fitness effects of myoglobin. by Whitney Wong (20642739)

“…<p>(A) Output of a spherically symmetrical model of the coupled oxygen diffusion, myoglobin diffusion, oxygen/myoglobin binding, and aerobic respiration. …”

miR-184 expression in the central and peripheral nervous system. by Sravya Paluri (5475260)

Grid division diagram. by Ming Zhang (9736)

“…Between the second and third sand-blocking fences, when the height of sand-blocking fence is 2.5m, the increase of wind speed is 13.87% lower than that of 2m height. The decrease is the largest, and sand particles are easy to deposit here in large quantities. …”

Model calculation diagram. by Ming Zhang (9736)

“…Between the second and third sand-blocking fences, when the height of sand-blocking fence is 2.5m, the increase of wind speed is 13.87% lower than that of 2m height. The decrease is the largest, and sand particles are easy to deposit here in large quantities. …”