The REsource for Materials Informatics (REMI) will host a diverse collection of scripting notebooks (Jupyter, Matlab LiveScripts, etc.) for collecting, pre-processing, analyzing, and visualizing materials data. Notebooks are curated using tags aligned to Materials Science and Data Science topics. REMI emerged from the realization that both experts and novices wanted examples of using machine learning for science. Meanwhile, lots of experts are developing digital notebooks (e.g. Jupyter) to demonstrate step-by-step data collection, pre-processing, analysis and visualization.

The open dataset, software, and other files accompanying the manuscript "An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models," submitted for publication to Integrated Materials and Manufacturing Innovations.Machine learning and autonomy are increasingly prevalent in materials science, but existing models are often trained or tuned using idealized data as absolute ground truths.

Data set from peer-reviewed publication: D. M. Bailey et al., Precision spectroscopy of nitrous oxide isotopocules with a cross-dispersed spectrometer and a mid-infrared frequency comb, Analytical Chemistry, 92, 13759-13766 (2020).

Results of single-cell and single-transcript measurements collected as part of the Bias and Resolvability Attribution using Split Samples (BRASS) study. Each file is a list of fluorescence intensities per cell (or estimated RNA counts per cell), for each of 12 different methods, for 3 biological replicates.

The NIST Registry of Adsorbent Materials is a free, web-based catalog of adsorbent materials and metadata describing those adsorbent materials. Each adsorbent material in the registry is assigned a registry ID to 1) allow unique identification adsorbents independent of arbitrary naming schemes (e.g., HKUST-1, CuBTC, Basolate C300® are the same material and have the same registry ID) and 2) enable cross referencing information about each material from outside databases and material registries.

This data is provided as a supplement to NIST Technical Note 2140, AWS-3 LTE Impacts on AMT, which presents test results assessing the impact of adjacent-band long term evolution (LTE) emissions on aeronautical mobile telemetry (AMT) systems. The data provided here is sufficient to reproduce all of the plots and analyses in Chapters 5 and 6, and Appendices B, C, D, and E, which present the data analysis results. All files are spreadsheets in comma-separated values (CSV) format, with labeled columns for various key performance indicators (KPIs) and test configurations.

Open source software for solving large-scale generalized eigenvalue problems on distributed computers. Suitable for large (80,000 by 80,000 or greater) dense matrices. Written in Fortran90+. Includes a test program and sample output. In particular, this distribution, MPI_GEVP_Package.tar.gz, consists of documentation (HowTo_MPI_GEVP_inviter.pdf), a collection of output files, and the software distribution itself.

User Equipment (UE)-to-Network Relay functionality was introduced in Release 13 of the Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) to extend and improve user connectivity. A UE connected to the network (relay UE) provides network access to another UE (remote UE) by establishing a direct one-to-one connection with it using the sidelink (SL) channels over the PC5 interface. The PC5 signaling is used for the establishment and maintenance of this direct connection, and different procedures and timers are linked to these processes.

The datasets contain the following parts for Open Media Forensics Challenge (OpenMFC) evaluations:1. NC16 Kickoff dataset2. NC17 development and evaluation datasets3. MFC18 development and evaluation datasets4. MFC19 development and evaluation datasets5. MFC20 development and evaluation datasets6. OpenMFC2022 steg datasets7. OpenMFC2022 deepfake datasets

This data set contains the data and scripts used to generate the tables in NIST Technical Note 2092. The tables as built by these scripts will differ from those in the published report in terms of formatting and will be in html, but otherwise will be identical to those in NIST Technical Note 2092.

The zip file contains five files, including R Data files containing data used in the analysis, an R Markdown script file, and R Data files containing results of the analysis.