The 24 Hour Box zipfile contains images of daily Trademark applications in either JPG format (black and white, grayscale, or color) or TIFF format (black and white) processed through the Trademark Image Capture and Retrieval System (TICRS) and viewable using any standard image viewer. JPG images are accompanied by an XML file that contains text information about the application. TIFF images contain the application number embedded in the image (stamped prior to scan).

Pending and registered trademark text data (no images) to include word mark, serial number, registration number, filing date, registration date, goods and services, classification number(s), status code(s), and design search code(s).

Assignment (ownership) text data (no images).

Trademark Trial and Appeal Board (TTAB) text data (no images).

Contains detailed information on 7.0 million trademark applications filed with or registrations issued by the USPTO between 1870 and 2020.

Contains detailed information on 786,931 assignments and other transactions recorded at the USPTO between 1952 and 2020 and involving 1,491,485 million unique trademark properties.

ANDiE the Autonomous Neutron Diffraction Explorer is a tool for autonomously discovering the magnetic transition temperature and transition dynamics of a material from neutron diffraction experiments. The Jupyter notebooks used to implement ANDiE can be found here: https://github.com/usnistgov/ANDiE-v1_0 The Jupyter notebooks contained therein are of ANDiE as implemented at the WAND2 instrument at the HB-2C beamline at the High-Flux Isotope Reactor (HFIR) at Oak Ridge National Laboratory (ORNL).

Round 8 Test 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. 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 360 QA AI models using a small set of model architectures.

Round 8 Holdout 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 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.

Here we provide curated analytical chemistry data for eggs collected from 1999 to 2010 on a subset of species and analytes that were measured regularly and reasonably systematically. Included in this publication are 487 samples analyzed for 174 ubiquitous environmental contaminants such as (poly)brominated diphenyl ethers (BDEs), mercury, organochlorine pesticides (OCPs), and polychlorinated biphenyls (PCBs). Data were collated to form a dataset useful in chemometric and related analyses of the marine ecosystem in the north Pacific Ocean.