This is a searchable historical collection of standards referenced in regulations - Voluntary consensus standards, government-unique standards, industry standards, and international standards referenced in the Code of Federal Regulations (CFR).

The Toxicity Reference Database (ToxRefDB) contains approximately 30 years and $2 billion worth of animal studies. ToxRefDB allows scientists and the interested public to search and download thousands of animal toxicity testing results for hundreds of chemicals that were previously found only in paper documents. Currently, there are 474 chemicals in ToxRefDB, primarily the data rich pesticide active ingredients, but the number will continue to expand.

The Human Protein Reference Database (HPRD) represents a centralized platform to visually depict and integrate information pertaining to domain architecture, post-translational modifications, interaction networks and disease association for each protein in the human proteome.

These are the curated 16S and gyrB datasets created from the NCBI refseq database. These two datasets have only the sequences of either the 16S gene (16S_refseq.fa) or the gyrB gene (gyr_refseq.fa).

The NIST Chemistry WebBook provides users with easy access to chemical and physical property data for chemical species through the internet. The data provided in the site are from collections maintained by the NIST Standard Reference Data Program and outside contributors. Data in the WebBook system are organized by chemical species. The WebBook system allows users to search for chemical species by various means. Once the desired species has been identified, the system will display data for the species. Data include thermochemical properties of species and reactions, thermophysical properties of species, and optical, electronic and mass spectra.

Background The EST database provides a rich resource for gene discovery and in silico expression analysis. We report a novel computational approach to identify co-expressed genes using EST database, and its application to IL-8. Results IL-8 is represented in 53 dbEST cDNA libraries. We calculated the frequency of occurrence of all the genes represented in these cDNA libraries, and ranked the candidates based on a Z-score. Additional analysis suggests that most IL-8 related genes are differentially expressed between non-tumor and tumor tissues. To focus on IL-8's function in tumor tissues, we further analyzed and ranked the genes in 16 IL-8 related tumor libraries. Conclusions This method generated a reference database for genes co-expressed with IL-8 and could facilitate further characterization of functional association among genes.

built as described here: https://github.com/nasa/GeneLab_Data_Processing/blob/master/Metagenomics/Estimate_host_reads_in_raw_data/Workflow_Documentation/SW_MGEstHostReads/reference-database-info.md

Test fastq files hold 4 read pairs: 1 phage, 1 e. coli, 1 human, 1 mouse

FASTA file with the sequences for amphibian, fish and reptile species registered to occur in Mexico City, functioning as a custom reference database for the Meta16S metabarcoding library. Sanger sequences were obtained through DNA extractions from preserved tissues (obtained through the project's collaborators) using the QIAGEN DNeasy Blood & Tissue kit. The processing of the sequences (including de novo assembly) was done using Geneious Prime. Sequences were used during the Taxonomy Assignment step of the bioinformatic pipeline using Python packages BLAST+ and BASTA.

Marker Gene Reference Database For Dix-seqTable of ContentsITS-2024.4 (https://unite.ut.ee/index.php)RDP_16S_V18 (https://doi.org/10.1093/nar/24.1.82)

The LANDFIRE Reference Database (LFRDB) is a database of geo-referenced field data (plots) that describe vegetation and fuel attributes for a given area. The LFRDB provides “ground-truth” data for mapping and modeling vegetation. In LF 2016 Remap, improvements to the LFRDB include new data contributors, many more plots, increased distribution of plots, an updated Auto-Key Program, and the addition of geographic and geophysical variables. Learn more about the LFRDB here https://landfire.gov/lfrdb_data.php

Note: we recommend switching the view from 'Table' to 'Tree' when exploring the dataset. Further, we refer to https://www.kuleuven.be/rdm/en/rdr/large-downloads for efficient download options. The dataset contains a suite of large-eddy simulation results of a wind farm operating in conventionally neutral boundary layers, in which atmospheric conditions are varied to study the effect of wind-farm blockage and self-induced gravity waves. A 1.6GW offshore wind farm with a fixed layout, composed of 160 IEA 10MW turbines, is considered for 36 different atmospheric stratification conditions. In particular, we initialize the simulations with four capping-inversion heights (i.e. 150, 300, 500 and 1000 m), three capping-inversion strengths (i.e. 2, 5 and 8 K) and three free-atmosphere lapse rates (i.e. 1, 4 and 8 K/km), while the geostrophic wind is fixed to 10 m/s. In addition, there are four simulations without atmospheric stratification, four simulations which consider a single turbine only and five simulations that use a different farm layout (note that the latter are not illustrated in Lanzilao & Meyers (2024)), for a total of 49 cases. All simulations are performed by using a concurrent precursor method. Hence, the inflow conditions in the main domain (the one containing the turbines) are provided by the flow fields generated in the precursor domain. Appropriate spin-ups are used (first in the precursor domain, and subsequently in precursor and main domains) to generate fully developed turbulence in the boundary layer. The dataset is generated with the SP-Wind code, an in-house LES and DNS code developed at KU Leuven. For details of the code structure and simulation set-up we refer to Lanzilao & Meyers (2024). The dataset is organized as follows. The results obtained in the 49 simulations are divided into 49 folders. Each folder contains results obtained on both the precursor (stat_precursor_**.h5) and main (stat_main_**.h5) domains. There are 42 time-averaged flow fields per domain, which are categorized in first-, second- and third-order statistics, further divided into resolved and sub-grid scale components. The flow fields have dimensions of Nx x Ny x Nz, where Nx, Ny and Nz are the number of grid points in the streamwise, spanwise and vertical directions used in the respective domain. Note that these flow fields are time-averaged over the last 1.5 hours of the simulation. In addition, the inst_precursor_first_order.h5 and inst_main_first_order.h5 files provide the instantaneous velocity and potential temperature fields obtained at the end time of the simulations. Finally, the turbine_data.h5 file contains information about the thrust, power and orientation of all turbines in the farm. For more information, we refer to the readme.txt file located in the dataset and to Lanzilao & Meyers (2024). Acknowledgements The authors acknowledge support from the Research Foundation Flanders (FWO, Grant No. G0B1518N), from the project FREEWIND, funded by the Energy Transition Fund of the Belgian Federal Public Service for Economy, SMEs, and Energy (FOD Economie, K.M.O., Middenstand en Energie) and from the European Union Horizon Europe Framework programme (HORIZON-CL5-2021-D3-03-04) under grant agreement no. 101084205. The computational resources and services in this work were provided by the VSC (Flemish Supercomputer Center), funded by the Research Foundation Flanders (FWO) and the Flemish Government department EWI. References Lanzilao, L. & Meyers, J. (2024), A parametric large-eddy simulation study of wind-farm blockage and gravity waves in conventionally neutral boundary layers. J. Fluid Mech. (2024), vol. 979, A54, doi:10.1017/jfm.2023.1088

Additional file 3. Taxonomic profiling of metagenome samples from hunter-gatherers and Western populations using the CCMetagen pipeline.

A comprehensive, integrated, non-redundant, well-annotated set of reference sequences including genomic, transcript, and protein.

16S metabarcoding databases and naive-bayes classifiers specific to the V4-V5 region. Built from the Silva 138.1 SSU Ref NR 99 database using Qiime2 (version 2023.2 and 2023.5) and the q2-clawback plugin. Includes weighted classifiers for two Earth Microbiome Project Ontology (EMPO) 3 habitat types: "sediment (saline)" and "water (saline)" , with data downloaded from Qiita. Sequences were not dereplicated.

Primers used:

EMP 16S 515f: GTGYCAGCMGCCGCGGTAA

EMP 16S 926r: CCGYCAATTYMTTTRAGTTT

Stats

286,948 unique sequences

388,496 total sequences

46,254 unique taxa (Level 7)

This dataset is a mapping between MEANS-InOut input data and Life Cycle Inventories from reference databases (Agribalyse, ecoinvent). The MEANS-InOut input data are agricultural production system inputs (fertilisers, plant protection products, agricultural operations, livestock feed, ingredients to be incorporated into livestock feed, etc.). Each input is associated with one or more LCI, which represent(s) the impacts of the production of this input, and the database from which the LCI(s) is from. This version of the dataset corresponds to the following versions of the databases: Agribalyse v3.1.1 and ecoinvent v3.9. The correspondence file (named mapping_data.tab) is associated with : a document describing the input types in the MEANS-InOut software (file: Input_type_description.pdf), a document describing how the value of the input flow of a LCI for an agricultural system studied in MEANS-InOut is obtained from the value taken by this input in MEANS-InOut. (file: LCI_value_construction.pdf) Ce jeu de données établit la correspondance entre les référentiels de MEANS-InOut et des Inventaires de Cycle de Vie de base de données de référence (Agribalyse, ecoinvent). Les référentiels de MEANS-InOut sont des intrants des systèmes de production agricole (engrais, produits phytosanitaires, opérations agricoles, aliments du bétail, ingrédients à incorporer dans les aliments composés...). A chaque intrant est associé un ou plusieurs ICV, qui représentent les impacts de la production de cet intrant, et la base de données dont le ou les ICV sont issus. Cette version du jeu de données fait la correspondance avec les versions suivantes des bases de données : Agribalyse v3.1.1 et ecoinvent v3.9. Au fichier de correspondances (fichier : mapping_data.tab), sont associés : un document qui décrit les types d'intrants du logiciel MEANS-InOut (fichier : Input_type_description.pdf), un document qui décrit comment est obtenue la valeur du flux des intrants d'un ICV d'un système agricole étudié dans MEANS-InOut à partir de la valeur prise par cet un intrant dans MEANS-InOut. (fichier : LCI_value_construction.pdf)

This service represents all sources in the source cadastre of North Rhine-Westphalia, independently managed by five institutions, or their sampling points based on the country’s water stationing map (gsk3c). The attribute table provides information about the number, location and data holders of all objects displayed within a source area and shows the reference source. Sources from Geobasis NRW — i.e. from the state survey — are always reference sources. All objects captured in a radius of 10 m around the reference source are merged under a source NRW_ID. Overlapping radii are combined into a larger contiguous source area. If there is no reference source of Geobasis NRW in an area, the source closest to the area centre of gravity represents the reference source.

The National Software Reference Library (NSRL) collects software from various sources and incorporates file profiles computed from this software into a Reference Data Set (RDS) of information. The RDS can be used by law enforcement, government, and industry organizations to review files on a computer by matching file profiles in the RDS. This alleviates much of the effort involved in determining which files are important as evidence on computers or file systems that have been seized as part of criminal investigations. The RDS is a collection of digital signatures of known, traceable software applications. There are application hash values in the hash set which may be considered malicious, i.e. steganography tools and hacking scripts. There are no hash values of illicit data, i.e. child abuse images.

The zip file contains the benchmark data used for the TIPP3 simulation study. See the README file for more information.

A global reference dataset on cropland was collected through a crowdsourcing campaign implemented using Geo-Wiki. This reference dataset is based on a systematic sample at latitude and longitude intersections, enhanced in locations where the cropland probability varies between 25-75% for a better representation of cropland globally. Over a three week period, around 36K samples of cropland were collected. For the purpose of quality assessment, additional datasets are provided. One is a control dataset of 1793 sample locations that have been validated by students trained in image interpretation. This dataset was used to assess the quality of the crowd validations as the campaign progressed. Another set of data contains 60 expert or gold standard validations for additional evaluation of the quality of the participants. These three datasets have two parts, one showing cropland only and one where it is compiled per location and user. This reference dataset will be used to validate and compare medium and high resolution cropland maps that have been generated using remote sensing. The dataset can also be used to train classification algorithms in developing new maps of land cover and cropland extent.

Combustion calorimetry is the main method for the determination of enthalpies of formation for organic compounds. Rigorous application of the method uses a 100-step procedure, sometimes called Washburn corrections, to convert the experimental results into standard thermodynamic quantities. Because every laboratory uses its own in-house software implementing this procedure, which is often not available for verification or testing, it is difficult to fully assess experimental results. Furthermore, these programs often use obsolete reference values of thermodynamic properties. This Standard Reference Database (SRD) introduces a standard procedure for this conversion. All experimental data used in this procedure (second virial coefficients of gas mixtures, densities, solubilities of gases in water and electrolyte solutions, etc.) have been reviewed by NIST personnel and the most reliable values have been recommended. The working equations were revised where necessary. Consistent with the NIST efforts on developing publication standards, this SRD also provides a resource essential to reproducible publications and interlaboratory exchange of the combustion calorimetry results. The primary users are thermochemical laboratories worldwide. This SRD will also benefit current practitioners in industry and future investigators through incorporation into university coursework. Please see the supporting publication for details: doi:10.1016/j.jct.2021.106425