During work on the new reference equation of state for carbon dioxide [A.H. Harvey, S.A. Tashkun, R. Hellmann, and E.W. Lemmon, J. Phys. Chem. Ref. Data, in preparation], we obtained unpublished data from several sources. These represent numerical values for data only presented graphically in a publication, or in some cases data not present in the publication at all. With the permission of the authors, we document and deposit these data here so they will be available for future workers.

This is the data presented in the figures of the paper "The effects of advanced spectral line shapes on atmospheric carbon dioxide retrievals" published in J. Quant. Spectrosc. Radiat. Transfer at https://doi.org/10.1016/j.jqsrt.2022.108324

Round 10 Train DatasetThis is the training data used to create and evaluate trojan detection software solutions. This data, generated at NIST, consists of object detection AIs trained on the COCO dataset. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers. This dataset consists of 144 AI models using a small set of model architectures.

Round 9 Test DatasetThis is the test data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Round 9 Holdout DatasetThis is the holdout data used to evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform one of three tasks, sentiment classification, named entity recognition, or extractive question answering on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Speed of Sound Measurements of Binary Mixtures of Difluoromethane (R-32) with 2,3,3,3-Tetrafluoropropene (R-1234yf) or trans-1,3,3,3-Tetrafluoropropene (R-1234ze(E)) Refrigerants

Round 8 Train DatasetThis is the training data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform extractive question answering (QA on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.

Data corresponding to Figs. 2,3,5,6,11,12,13,14 for Fleurbaey et al J. Quant Spectrosc. Radiat. Transf. vol 270, 107684 (2021). doi.org.10.1016/j.jqsrt.2021.107684

Dataset for generation of figures in Air-broadening in near-infrared carbon dioxide line shapes: quantifying contributions from O2, N2, and Ar (https://doi.org/10.1016/j.jqsrt.2021.107669)

Round 7 Holdout DatasetThis is the holdout data used to construct and evaluate trojan detection software solutions. This data, generated at NIST, consists of natural language processing (NLP) AIs trained to perform named entity recognition (NER) on English text. A known percentage of these trained AI models have been poisoned with a known trigger which induces incorrect behavior. This data will be used to develop software solutions for detecting which trained AI models have been poisoned via embedded triggers.