Mendeley Data Showcase

Technology offers significant opportunities, particularly for students who struggle with mathematics, dislike the subject, are disinterested, have learning difficulties, make frequent mistakes, have misconceptions, or lack self-confidence. Digital avatars can encourage critical thinking by providing students with correct or incorrect solutions, increase metacognitive awareness, support self-assessment, and help them recognize their own mistakes. Prospective teachers should gain experience with such tools because they play a crucial role in shaping and designing their future classrooms. This study aims to examine how middle school students, guided by prospective teachers, respond to incorrect mathematical solutions presented by digital avatars. Prospective teachers mentored 15 middle school students at a Turkish middle school and implemented an avatar-supported group-based activity. The teacher candidates comprised two males and four females, aged between 20 and 22. These teacher candidates, all second-year undergraduate students in the elementary mathematics education department, served as mentors to the middle school students. The students, consisting of seven boys and eight girls aged 11 to 12, were all enrolled in the 5th grade at a public middle school. Students collaboratively created avatars and programmed these avatars to knowingly solve fraction problems incorrectly. The students then compared their own solutions with the avatars' incorrect solutions. Data collection tools included interviews conducted by prospective teachers with students, interviews conducted by researchers with prospective teachers, and participant observation.

This dataset contains data of soil carbon, nitregon, soil erosion, connectivity on loess hillslope. We hypothesis there are different variation pattern and thresholds of hillslope connectivity for soil carbon and nitregion between planted shrub hillslope and grassland hillslope after revegation of abandoned crop planting. This dataset is the basic dataset for this study.

Monitoring of GD2-CAR T-cell killing in the Incucyte live cell imaging system for five days, showing rapid and effective tumor clearance in real time as soon as 24 h.

This dataset comprises 1,257 high-quality images of four types of vegetables: Malabar Spinach, Brinjal (Eggplant), Cauliflower, and Bean. Each vegetable class is further divided into two categories based on condition: Healthy and Unhealthy. Structure: Vegetables Dataset/ ├── Malabar/ │ ├── Healthy/ │ └── Unhealthy/ ├── Brinjal/ │ ├── Healthy/ │ └── Unhealthy/ ├── Cauliflower/ │ ├── Healthy/ │ └── Unhealthy/ └── Bean/ ├── Healthy/ └── Unhealthy/ Data Collection Details: Devices Used: iPhone 11 and Samsung Galaxy S21 FE Collection Locations: Ashulia, Karwan Bazar, Kathalbagan Bazar, Dhaka,Bangladesh. The dataset was created to support image classification tasks for detecting the health status of vegetables, which can aid in agricultural quality control or automated market inspection systems.

Research Data for the manuscript entitled "Synergistic Interfacial and Multi-omics Regulation of La3+/Gd3+ Biomineralization by Micromonospora auratinigra"

PPP4R4-like protein; Leishmania infantum (strain JPCM5)

AAA ATPase domain-containing protein; Leishmania infantum (strain JPCM5)

CFAP106A; Leishmania infantum (strain JPCM5)

RING-type E3 ligase 3; Leishmania infantum (strain JPCM5)

DnaJ domain-containing protein | JDP71 | J71; Leishmania infantum (strain JPCM5)