The BUTTER Empirical Deep Learning Dataset represents an empirical study of the deep learning phenomena on dense fully connected networks, scanning across thirteen datasets, eight network shapes, fourteen depths, twenty-three network sizes (number of trainable parameters), four learning rates, six minibatch sizes, four levels of label noise, and fourteen levels of L1 and L2 regularization each. Multiple repetitions (typically 30, sometimes 10) of each combination of hyperparameters were preformed, and statistics including training and test loss (using a 80% / 20% shuffled train-test split) are recorded at the end of each training epoch. In total, this dataset covers 178 thousand distinct hyperparameter settings ("experiments"), 3.55 million individual training runs (an average of 20 repetitions of each experiments), and a total of 13.3 billion training epochs (three thousand epochs were covered by most runs). Accumulating this dataset consumed 5,448.4 CPU core-years, 17.8 GPU-years, and 111.2 node-years.

IEA Web Services (EAWS) provides for the maintenance of content for EA information on Energy.Gov, Powerpedia.

EHSS Web Services (AUWS) provides for the maintenance of content for EHSS information on Energy.Gov, Powerpedia and the EHSS intranet.

This data is provided by TfNSW Roads and Maritime and provides dates, locations and session times for face to face workshops. The [Roads and Maritime](https://www.rms.nsw.gov.au/maritime/safety-rules/education-program/lifejacket-clinics.html) site provides detailed information about the clinics, the importance of servicing your lifejacket, an online video tutorial, and how to register for a face to face workshop. The clinics are run as part of the NSW Government's 'Wear a Lifejacket' campaign, and in line with the [Maritime Safety Plan](https://future.transport.nsw.gov.au/plans).

Resources for MHKDR data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of the MHKDR. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting MHKDR metadata for federation or inclusion in their local catalogs.

National Training Center provides for the infrastructure and applications necessary to develop and deliver safety and security training courses across the department.

Resources for OEDI data submitters and curators, including training videos, step-by-step guides on data submission, and detailed documentation of OEDI. The Data Management and Submission Best Practices document also contains API access and metadata schema information for developers interested in harvesting OEDI metadata for federation or inclusion in their local catalogs.

This is metadata documentation for the Quality Assurance Training Tracking System (QATTS) which tracks Quality Assurace training given by R7 QA staff to in-house staff and external partners.

This data set provides resources for state employment and training throughout United States.

This submission contains several shapefiles used for a deterministic PFA, as well as a heat composite risk segment with union overlay, and training sites used for weights of evidence. More detailed metadata can be found in the specific file.