Base information

AEZAKMI V3 is build on top of AEZAKMI V2 but there are many new samples. I removed all coding samples plus those with "BEGINCONTEXT ENDCONTEXT References:" as they were bugging out the training with longer sequence len. I included filtered no_robots_sharegpt dataset, which makes this dataset non-commercial only! From no_robots, I removed stories, mentions of AI, coding etc. I added wsb dataset, based on Sentdex/wsb_reddit_v001, but I removed all samples shorter… See the full description on the dataset page: https://huggingface.co/datasets/adamo1139/AEZAKMI_v3.

5' RNASeq of mRNA from Shewanella putrefaciens CN-32 grown aerobically in Luria-Bertani broth (LB) and defined lactate minimal medium 5'-end mRNA profiles of mid-log phase bacterial cells growing in LB or lactate medium were generated by next-generation sequencing.

These are the replication files for "The Desire for Social Status and Economic Conservatism Among Affluent Americans." They include the datasets that were analyzed, the R code that was used to produce the results, and codebooks that detail the coding of all variables included in the datasets.

Pseudo-code of the generalised dynamic programming forward and reversed passes. and indicate 'th row and 'th column of matrix.

Computational chemistry instructional activities are often based around students running chemical simulations via a graphical user interface (GUI). GUI-based activities offer many advantages, as they enable students to run chemical simulations with a few mouse clicks. Although these activities are excellent for introducing students to the capabilities of chemical simulations, the disadvantage is that the students’ experience is not representative of how professional computational chemists work. Just as it is important that students in an organic chemistry instructional lab gain hands-on experience with equipment commonly used by professional organic chemists, students of computational chemistry must gain hands-on experience with coding, as professional computational chemists do not rely on GUIs; we write code. Motivated by the need for instructional activities that provide hands-on experience with computer code, a pair of activities were created around a free lightweight (runs on standard laptops) open-source Lennard-Jones (LJ) fluid simulation code written in Python, a programming language that prioritizes readability. The first activity, aimed at undergraduate physical chemistry lab courses, involves students writing Python code in a Jupyter Notebook that is used to run LJ simulations and fit a van der Waals gas model to data produced by the LJ fluid simulations. The second is a jigsaw activity, aimed at advanced undergraduate or graduate students, where students are assigned different sections of the LJ fluid simulation code, and must demonstrate the functionality of their section to the class by both giving an oral presentation and sharing a Jupyter Notebook demonstration of their own design.

Pseudo-code of the sparse matrix generation procedure.

Summary of qualitative coding tree.

Data capture and coding summary.

Percentage of the sample that used each metaphoric source category by religion and in total, in order of frequency from left (most) to right (least).

Inductive content analysis including all responses, codes and categories for the qualitatively coded behavioural responses. (XLSX)