UpperMyo is an open six-muscle surface electromyography (sEMG) dataset for upper-limb action detection, temporal prediction, and cross-dataset transfer, evaluated through internal validation and paired external experiments. Analyses use 264 recording blocks from 24 healthy male participants, sampled at 1 kHz with 12-bit resolution; the nine-class dataset contains 2,640 four-second samples spanning eight actions and background. Internal evaluations cover recognition, channel ablation, and elbow-flexion/rest temporal classification. A convolutional neural network (CNN) achieved a macro-F1 of 0.9860 under nested leave-one-subject-out validation. Temporal classification yielded CNN accuracies of 89.5% and 75.3% at 0 and 2,000 ms backward offsets from an sEMG onset reference. Four external experiments compare transfer with target-only learning at identical labeling budgets for matched actions, new actions, activities without class mapping, and continuous grip-force estimation. WristMotion balanced accuracy increased by 14.21 percentage points with four labeled trials; mean flexor carpi ulnaris (FCU) grip-force R2 increased from 0.6431 to 0.6899 at the full budget. Transfer yielded gains across multiple tasks and budgets, with benefits varying by task and labeling budget. UpperMyo provides traceable data and evaluation evidence for sEMG action analysis and cross-dataset learning.