There are several Microsoft Word documents here detailing data creation methods and with various dictionaries describing the included and derived variables.The Database Creation Description is meant to walk a user through some of the steps detailed in the SAS code with this project.The alphabetical list of variables is intended for users as sometimes this makes some coding steps easier to copy and paste from this list instead of retyping.The NIS Data Dictionary contains some general dataset description as well as each variable's responses.

This data includes the location of cooling towers registered with New York State. The data is self-reported by owners/property managers of cooling towers in service in New York State. In August 2015 the New York State Department of Health released emergency regulations requiring the owners of cooling towers to register them with New York State. In addition the regulation includes requirements: regular inspection; annual certification; obtaining and implementing a maintenance plan; record keeping; reporting of certain information; and sample collection and culture testing. All cooling towers in New York State, including New York City, need to be registered in the NYS system. Registration is done through an electronic database found at: www.ny.gov/services/register-cooling-tower-and-submit-reports. For more information, check http://www.health.ny.gov/diseases/communicable/legionellosis/, or go to the “About” tab.

The Pesticide Data Program (PDP) is a national pesticide residue database program. Through cooperation with State agriculture departments and other Federal agencies, PDP manages the collection, analysis, data entry, and reporting of pesticide residues on agricultural commodities in the U.S. food supply, with an emphasis on those commodities highly consumed by infants and children.This dataset provides information on where each tested sample was collected, where the product originated from, what type of product it was, and what residues were found on the product, for calendar years 1992 through 2023. The data can measure residues of individual compounds and classes of compounds, as well as provide information about the geographic distribution of the origin of samples, from growers, packers and distributors. The dataset also includes information on where the samples were taken, what laboratory was used to test them, and all testing procedures (by sample, so can be linked to the compound that is identified). The dataset also contains a reference variable for each compound that denotes the limit of detection for a pesticide/commodity pair (LOD variable). The metadata also includes EPA tolerance levels or action levels for each pesticide/commodity pair. The dataset will be updated on a continual basis, with a new resource data file added annually after the PDP calendar-year survey data is released.Resources in this dataset:Resource Title: CSV Data Dictionary for PDP.File Name: PDP_DataDictionary.csv. Resource Description: Machine-readable Comma Separated Values (CSV) format data dictionary for PDP Database Zip files. Defines variables for the sample identity and analytical results data tables/files. The ## characters in the Table and Text Data File name refer to the 2-digit year for the PDP survey, like 97 for 1997 or 01 for 2001. For details on table linking, see PDF. Resource Software Recommended: Microsoft Excel,url: https://www.microsoft.com/en-us/microsoft-365/excelResource Title: Data dictionary for Pesticide Data Program. File Name: PDP DataDictionary.pdf. Resource Description: Data dictionary for PDP Database Zip files. Resource Software Recommended: Adobe Acrobat, url: https://www.adobe.comResource Title: 2023 PDP Database Zip File. File Name: 2023PDPDatabase.zipResource Title: 2022 PDP Database Zip File. File Name: 2022PDPDatabase.zipResource Title: 2021 PDP Database Zip File. File Name: 2021PDPDatabase.zipResource Title: 2020 PDP Database Zip File. File Name: 2020PDPDatabase.zipResource Title: 2019 PDP Database Zip File. File Name: 2019PDPDatabase.zipResource Title: 2018 PDP Database Zip File. File Name: 2018PDPDatabase.zipResource Title: 2017 PDP Database Zip File. File Name: 2017PDPDatabase.zipResource Title: 2016 PDP Database Zip File. File Name: 2016PDPDatabase.zipResource Title: 2015 PDP Database Zip File. File Name: 2015PDPDatabase.zipResource Title: 2014 PDP Database Zip File. File Name: 2014PDPDatabase.zipResource Title: 2013 PDP Database Zip File. File Name: 2013PDPDatabase.zipResource Title: 2012 PDP Database Zip File. File Name: 2012PDPDatabase.zipResource Title: 2011 PDP Database Zip File. File Name: 2011PDPDatabase.zipResource Title: 2010 PDP Database Zip File. File Name: 2010PDPDatabase.zipResource Title: 2009 PDP Database Zip File. File Name: 2009PDPDatabase.zipResource Title: 2008 PDP Database Zip File. File Name: 2008PDPDatabase.zipResource Title: 2007 PDP Database Zip File. File Name: 2007PDPDatabase.zipResource Title: 2006 PDP Database Zip File. File Name: 2006PDPDatabase.zipResource Title: 2005 PDP Database Zip File. File Name: 2005PDPDatabase.zipResource Title: 2004 PDP Database Zip File. File Name: 2004PDPDatabase.zipResource Title: 2003 PDP Database Zip File. File Name: 2003PDPDatabase.zipResource Title: 2002 PDP Database Zip File. File Name: 2002PDPDatabase.zipResource Title: 2001 PDP Database Zip File. File Name: 2001PDPDatabase.zipResource Title: 2000 PDP Database Zip File. File Name: 2000PDPDatabase.zipResource Title: 1999 PDP Database Zip File. File Name: 1999PDPDatabase.zipResource Title: 1998 PDP Database Zip File. File Name: 1998PDPDatabase.zipResource Title: 1997 PDP Database Zip File. File Name: 1997PDPDatabase.zipResource Title: 1996 PDP Database Zip File. File Name: 1996PDPDatabase.zipResource Title: 1995 PDP Database Zip File. File Name: 1995PDPDatabase.zipResource Title: 1994 PDP Database Zip File. File Name: 1994PDPDatabase.zipResource Title: 1993 PDP Database Zip File. File Name: 1993PDPDatabase.zipResource Title: 1992 PDP Database Zip File. File Name: 1992PDPDatabase.zip

Objective: To develop a clinical informatics pipeline designed to capture large-scale structured EHR data for a national patient registry.

Materials and Methods: The EHR-R-REDCap pipeline is implemented using R-statistical software to remap and import structured EHR data into the REDCap-based multi-institutional Merkel Cell Carcinoma (MCC) Patient Registry using an adaptable data dictionary.

Results: Clinical laboratory data were extracted from EPIC Clarity across several participating institutions. Labs were transformed, remapped and imported into the MCC registry using the EHR labs abstraction (eLAB) pipeline. Forty-nine clinical tests encompassing 482,450 results were imported into the registry for 1,109 enrolled MCC patients. Data-quality assessment revealed highly accurate, valid labs. Univariate modeling was performed for labs at baseline on overall survival (N=176) using this clinical informatics pipeline.

Conclusion: We demonstrate feasibility of the facile eLAB workflow. EHR data is successfully transformed, and bulk-loaded/imported into a REDCap-based national registry to execute real-world data analysis and interoperability.

Methods eLAB Development and Source Code (R statistical software):

eLAB is written in R (version 4.0.3), and utilizes the following packages for processing: DescTools, REDCapR, reshape2, splitstackshape, readxl, survival, survminer, and tidyverse. Source code for eLAB can be downloaded directly (https://github.com/TheMillerLab/eLAB).

eLAB reformats EHR data abstracted for an identified population of patients (e.g. medical record numbers (MRN)/name list) under an Institutional Review Board (IRB)-approved protocol. The MCCPR does not host MRNs/names and eLAB converts these to MCCPR assigned record identification numbers (record_id) before import for de-identification.

Functions were written to remap EHR bulk lab data pulls/queries from several sources including Clarity/Crystal reports or institutional EDW including Research Patient Data Registry (RPDR) at MGB. The input, a csv/delimited file of labs for user-defined patients, may vary. Thus, users may need to adapt the initial data wrangling script based on the data input format. However, the downstream transformation, code-lab lookup tables, outcomes analysis, and LOINC remapping are standard for use with the provided REDCap Data Dictionary, DataDictionary_eLAB.csv. The available R-markdown ((https://github.com/TheMillerLab/eLAB) provides suggestions and instructions on where or when upfront script modifications may be necessary to accommodate input variability.

The eLAB pipeline takes several inputs. For example, the input for use with the ‘ehr_format(dt)’ single-line command is non-tabular data assigned as R object ‘dt’ with 4 columns: 1) Patient Name (MRN), 2) Collection Date, 3) Collection Time, and 4) Lab Results wherein several lab panels are in one data frame cell. A mock dataset in this ‘untidy-format’ is provided for demonstration purposes (https://github.com/TheMillerLab/eLAB).

Bulk lab data pulls often result in subtypes of the same lab. For example, potassium labs are reported as “Potassium,” “Potassium-External,” “Potassium(POC),” “Potassium,whole-bld,” “Potassium-Level-External,” “Potassium,venous,” and “Potassium-whole-bld/plasma.” eLAB utilizes a key-value lookup table with ~300 lab subtypes for remapping labs to the Data Dictionary (DD) code. eLAB reformats/accepts only those lab units pre-defined by the registry DD. The lab lookup table is provided for direct use or may be re-configured/updated to meet end-user specifications. eLAB is designed to remap, transform, and filter/adjust value units of semi-structured/structured bulk laboratory values data pulls from the EHR to align with the pre-defined code of the DD.

Data Dictionary (DD)

EHR clinical laboratory data is captured in REDCap using the ‘Labs’ repeating instrument (Supplemental Figures 1-2). The DD is provided for use by researchers at REDCap-participating institutions and is optimized to accommodate the same lab-type captured more than once on the same day for the same patient. The instrument captures 35 clinical lab types. The DD serves several major purposes in the eLAB pipeline. First, it defines every lab type of interest and associated lab unit of interest with a set field/variable name. It also restricts/defines the type of data allowed for entry for each data field, such as a string or numerics. The DD is uploaded into REDCap by every participating site/collaborator and ensures each site collects and codes the data the same way. Automation pipelines, such as eLAB, are designed to remap/clean and reformat data/units utilizing key-value look-up tables that filter and select only the labs/units of interest. eLAB ensures the data pulled from the EHR contains the correct unit and format pre-configured by the DD. The use of the same DD at every participating site ensures that the data field code, format, and relationships in the database are uniform across each site to allow for the simple aggregation of the multi-site data. For example, since every site in the MCCPR uses the same DD, aggregation is efficient and different site csv files are simply combined.

Study Cohort

This study was approved by the MGB IRB. Search of the EHR was performed to identify patients diagnosed with MCC between 1975-2021 (N=1,109) for inclusion in the MCCPR. Subjects diagnosed with primary cutaneous MCC between 2016-2019 (N= 176) were included in the test cohort for exploratory studies of lab result associations with overall survival (OS) using eLAB.

Statistical Analysis

OS is defined as the time from date of MCC diagnosis to date of death. Data was censored at the date of the last follow-up visit if no death event occurred. Univariable Cox proportional hazard modeling was performed among all lab predictors. Due to the hypothesis-generating nature of the work, p-values were exploratory and Bonferroni corrections were not applied.

This is Oxford University Press's most comprehensive single-volume dictionary, with 170,000 entries covering all varieties of English worldwide. The NODE data set constitutes a fully integrated range of formal data types suitable for language engineering and NLP applications: It is available in XML or SGML. - Source dictionary data. The NODE data set includes all the information present in the New Oxford Dictionary of English itself, such as definition text, example sentences, grammatical indicators, and encyclopaedic material. - Morphological data. Each NODE lemma (both headwords and subentries) has a full listing of all possible syntactic forms (e.g. plurals for nouns, inflections for verbs, comparatives and superlatives for adjectives), tagged to show their syntactic relationships. Each form has an IPA pronunciation. Full morphological data is also given for spelling variants (e.g. typical American variants), and a system of links enables straightforward correlation of variant forms to standard forms. The data set thus provides robust support for all look-up routines, and is equally viable for applications dealing with American and British English. - Phrases and idioms. The NODE data set provides a rich and flexible codification of over 10,000 phrasal verbs and other multi-word phrases. It features comprehensive lexical resources enabling applications to identify a phrase not only in the form listed in the dictionary but also in a range of real-world variations, including alternative wording, variable syntactic patterns, inflected verbs, optional determiners, etc. - Subject classification. Using a categorization scheme of 200 key domains, over 80,000 words and senses have been associated with particular subject areas, from aeronautics to zoology. As well as facilitating the extraction of subject-specific sub-lexicons, this also provides an extensive resource for document categorization and information retrieval. - Semantic relationships. The relationships between every noun and noun sense in the dictionary are being codified using an extensive semantic taxonomy on the model of the Princeton WordNet project. (Mapping to WordNet 1.7 is supported.) This structure allows elements of the basic lexical database to function as a formal knowledge database, enabling functionality such as sense disambiguation and logical inference. - Derived from the detailed and authoritative corpus-based research of Oxford University Press's lexicographic team, the NODE data set is a powerful asset for any task dealing with real-world contemporary English usage. By integrating a number of different data types into a single structure, it creates a coherent resource which can be queried along numerous axes, allowing open-ended exploitation by many kinds of language-related applications.

Each feature within this dataset is the authoritative representation of the location of a sample within the U.S. Department of Energy (DOE) Office of Legacy Management (LM) Environmental Database. The dataset includes sample locations from Puerto Rico to Alaska, with point features representing different types of sample locations such as boreholes, wells, geoprobes, etc. All sample locations are maintained within the LM Environmental Database, with feature attributes defined within the associated data dictionary.

We study the behaviour and cognition of wild apes and other species (elephants, corvids, dogs). Our video archive is called the Great Ape Dictionary, you can find out more here www.greatapedictionary.com or about our lab group here www.wildminds.ac.uk We consider these videos to be a data ark that we would like to make as accessible as possible. While we are unable to make the original video files open access at the present time you can search this database to explore what is available, and then request access for collaborations of different kinds by contacting us directly or through our website. We label all videos in the Great Ape Dictionary video archive with basic meta-data on the location, date, duration, individuals present, and behaviour present. Version 1.0.0 contains current data from the Budongo East African chimpanzee population (n=13806 videos). These datasets are being updated regularly and new data will be incorporated here with versioning. As well as the database there is a second read.me file which contains the ethograms used for each variable coded, and a short summary of other datasets that are in preparation for subsequent version(s). If you are interested in these data please contact us. Please note that not all variables are labeled for all videos, the detailed Ethogram categories are only available for a subset of data. All videos are labelled with up to 5 Contexts (at least one, rarely 5). If you are interested in finding a good example video for a particular behaviour, search for 'Library' = Y, this indicates that this clip contains a very clear example of the behaviour.

Dataset Overview

This dataset consists of 42,052 English words and their corresponding definitions. It is a comprehensive collection of words ranging from common terms to more obscure vocabulary. The dataset is ideal for Natural Language Processing (NLP) tasks, educational tools, and various language-related applications.

Key Features:

• Words: A diverse set of English words, including both rare and frequently used terms.
• Definitions: Each word is accompanied by a detailed definition that explains its meaning and contextual usage.

Total Number of Words: 42,052

Applications

This dataset is well-suited for a range of use cases, including:

• Natural Language Processing (NLP): Enhance text understanding models by providing contextual meaning and word associations.
• Vocabulary Building: Create educational tools or games that help users expand their vocabulary.
• Lexical Studies: Perform academic research on word usage, trends, and lexical semantics.
• Dictionary and Thesaurus Development: Serve as a resource for building dictionary or thesaurus applications, where users can search for words and definitions.

Data Structure

• Word: The column containing the English word.
• Definition: The column providing a comprehensive definition of the word.

Potential Use Cases

• Language Learning: This dataset can be used to develop applications or tools aimed at enhancing vocabulary acquisition for language learners.
• NLP Model Training: Useful for tasks such as word embeddings, definition generation, and contextual learning.
• Research: Analyze word patterns, rare vocabulary, and trends in the English language.

This version focuses on providing essential information while emphasizing the total number of words and potential applications of the dataset. Let me know if you'd like any further adjustments!

LScDC Word-Category RIG MatrixApril 2020 by Neslihan Suzen, PhD student at the University of Leicester (ns433@leicester.ac.uk / suzenneslihan@hotmail.com)Supervised by Prof Alexander Gorban and Dr Evgeny MirkesGetting StartedThis file describes the Word-Category RIG Matrix for theLeicester Scientific Corpus (LSC) [1], the procedure to build the matrix and introduces the Leicester Scientific Thesaurus (LScT) with the construction process. The Word-Category RIG Matrix is a 103,998 by 252 matrix, where rows correspond to words of Leicester Scientific Dictionary-Core (LScDC) [2] and columns correspond to 252 Web of Science (WoS) categories [3, 4, 5]. Each entry in the matrix corresponds to a pair (category,word). Its value for the pair shows the Relative Information Gain (RIG) on the belonging of a text from the LSC to the category from observing the word in this text. The CSV file of Word-Category RIG Matrix in the published archive is presented with two additional columns of the sum of RIGs in categories and the maximum of RIGs over categories (last two columns of the matrix). So, the file ‘Word-Category RIG Matrix.csv’ contains a total of 254 columns.This matrix is created to be used in future research on quantifying of meaning in scientific texts under the assumption that words have scientifically specific meanings in subject categories and the meaning can be estimated by information gains from word to categories. LScT (Leicester Scientific Thesaurus) is a scientific thesaurus of English. The thesaurus includes a list of 5,000 words from the LScDC. We consider ordering the words of LScDC by the sum of their RIGs in categories. That is, words are arranged in their informativeness in the scientific corpus LSC. Therefore, meaningfulness of words evaluated by words’ average informativeness in the categories. We have decided to include the most informative 5,000 words in the scientific thesaurus. Words as a Vector of Frequencies in WoS CategoriesEach word of the LScDC is represented as a vector of frequencies in WoS categories. Given the collection of the LSC texts, each entry of the vector consists of the number of texts containing the word in the corresponding category.It is noteworthy that texts in a corpus do not necessarily belong to a single category, as they are likely to correspond to multidisciplinary studies, specifically in a corpus of scientific texts. In other words, categories may not be exclusive. There are 252 WoS categories and a text can be assigned to at least 1 and at most 6 categories in the LSC. Using the binary calculation of frequencies, we introduce the presence of a word in a category. We create a vector of frequencies for each word, where dimensions are categories in the corpus.The collection of vectors, with all words and categories in the entire corpus, can be shown in a table, where each entry corresponds to a pair (word,category). This table is build for the LScDC with 252 WoS categories and presented in published archive with this file. The value of each entry in the table shows how many times a word of LScDC appears in a WoS category. The occurrence of a word in a category is determined by counting the number of the LSC texts containing the word in a category. Words as a Vector of Relative Information Gains Extracted for CategoriesIn this section, we introduce our approach to representation of a word as a vector of relative information gains for categories under the assumption that meaning of a word can be quantified by their information gained for categories.For each category, a function is defined on texts that takes the value 1, if the text belongs to the category, and 0 otherwise. For each word, a function is defined on texts that takes the value 1 if the word belongs to the text, and 0 otherwise. Consider LSC as a probabilistic sample space (the space of equally probable elementary outcomes). For the Boolean random variables, the joint probability distribution, the entropy and information gains are defined.The information gain about the category from the word is the amount of information on the belonging of a text from the LSC to the category from observing the word in the text [6]. We used the Relative Information Gain (RIG) providing a normalised measure of the Information Gain. This provides the ability of comparing information gains for different categories. The calculations of entropy, Information Gains and Relative Information Gains can be found in the README file in the archive published. Given a word, we created a vector where each component of the vector corresponds to a category. Therefore, each word is represented as a vector of relative information gains. It is obvious that the dimension of vector for each word is the number of categories. The set of vectors is used to form the Word-Category RIG Matrix, in which each column corresponds to a category, each row corresponds to a word and each component is the relative information gain from the word to the category. In Word-Category RIG Matrix, a row vector represents the corresponding word as a vector of RIGs in categories. We note that in the matrix, a column vector represents RIGs of all words in an individual category. If we choose an arbitrary category, words can be ordered by their RIGs from the most informative to the least informative for the category. As well as ordering words in each category, words can be ordered by two criteria: sum and maximum of RIGs in categories. The top n words in this list can be considered as the most informative words in the scientific texts. For a given word, the sum and maximum of RIGs are calculated from the Word-Category RIG Matrix.RIGs for each word of LScDC in 252 categories are calculated and vectors of words are formed. We then form the Word-Category RIG Matrix for the LSC. For each word, the sum (S) and maximum (M) of RIGs in categories are calculated and added at the end of the matrix (last two columns of the matrix). The Word-Category RIG Matrix for the LScDC with 252 categories, the sum of RIGs in categories and the maximum of RIGs over categories can be found in the database.Leicester Scientific Thesaurus (LScT)Leicester Scientific Thesaurus (LScT) is a list of 5,000 words form the LScDC [2]. Words of LScDC are sorted in descending order by the sum (S) of RIGs in categories and the top 5,000 words are selected to be included in the LScT. We consider these 5,000 words as the most meaningful words in the scientific corpus. In other words, meaningfulness of words evaluated by words’ average informativeness in the categories and the list of these words are considered as a ‘thesaurus’ for science. The LScT with value of sum can be found as CSV file with the published archive. Published archive contains following files:1) Word_Category_RIG_Matrix.csv: A 103,998 by 254 matrix where columns are 252 WoS categories, the sum (S) and the maximum (M) of RIGs in categories (last two columns of the matrix), and rows are words of LScDC. Each entry in the first 252 columns is RIG from the word to the category. Words are ordered as in the LScDC.2) Word_Category_Frequency_Matrix.csv: A 103,998 by 252 matrix where columns are 252 WoS categories and rows are words of LScDC. Each entry of the matrix is the number of texts containing the word in the corresponding category. Words are ordered as in the LScDC.3) LScT.csv: List of words of LScT with sum (S) values. 4) Text_No_in_Cat.csv: The number of texts in categories. 5) Categories_in_Documents.csv: List of WoS categories for each document of the LSC.6) README.txt: Description of Word-Category RIG Matrix, Word-Category Frequency Matrix and LScT and forming procedures.7) README.pdf (same as 6 in PDF format)References[1] Suzen, Neslihan (2019): LSC (Leicester Scientific Corpus). figshare. Dataset. https://doi.org/10.25392/leicester.data.9449639.v2[2] Suzen, Neslihan (2019): LScDC (Leicester Scientific Dictionary-Core). figshare. Dataset. https://doi.org/10.25392/leicester.data.9896579.v3[3] Web of Science. (15 July). Available: https://apps.webofknowledge.com/[4] WoS Subject Categories. Available: https://images.webofknowledge.com/WOKRS56B5/help/WOS/hp_subject_category_terms_tasca.html [5] Suzen, N., Mirkes, E. M., & Gorban, A. N. (2019). LScDC-new large scientific dictionary. arXiv preprint arXiv:1912.06858. [6] Shannon, C. E. (1948). A mathematical theory of communication. Bell system technical journal, 27(3), 379-423.

The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS (Natural Resources Conservation Service). The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings. The maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses. SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI® Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format that can be imported into a Microsoft® Access® database. A complete SSURGO dataset consists of:

GIS data (as ESRI® Shapefiles) attribute data (dbf files - a multitude of separate tables) database template (MS Access format - this helps with understanding the structure and linkages of the various tables) metadata

Resources in this dataset:Resource Title: SSURGO Metadata - Tables and Columns Report. File Name: SSURGO_Metadata_-_Tables_and_Columns.pdfResource Description: This report contains a complete listing of all columns in each database table. Please see SSURGO Metadata - Table Column Descriptions Report for more detailed descriptions of each column.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Metadata - Table Column Descriptions Report. File Name: SSURGO_Metadata_-_Table_Column_Descriptions.pdfResource Description: This report contains the descriptions of all columns in each database table. Please see SSURGO Metadata - Tables and Columns Report for a complete listing of all columns in each database table.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Data Dictionary. File Name: SSURGO 2.3.2 Data Dictionary.csvResource Description: CSV version of the data dictionary

Statistics of the sample of 6567 words from the website database.

Data Description: This data set contains all City of Cincinnati expenses by object code and day. The object code is the descriptor explaining the nature of the expense (personnel; overtime; office supplies; etc.)

Data Creation: This data is pulled directly from the City's financial software; which centralizes all department financial transactions city wide.

Data Created By: The Cincinnati Financial System (CFS)

Refresh Frequency: Daily

CincyInsights: The City of Cincinnati maintains an interactive dashboard portal, CincyInsights in addition to our Open Data in an effort to increase access and usage of city data. This data set has an associated dashboard available here: https://insights.cincinnati-oh.gov/stories/s/City-Spending/cuw9-nu34/

Data Dictionary: A data dictionary providing definitions of columns and attributes is available as an attachment to this dataset.

Processing: The City of Cincinnati is committed to providing the most granular and accurate data possible. In that pursuit the Office of Performance and Data Analytics facilitates standard processing to most raw data prior to publication. Processing includes but is not limited: address verification, geocoding, decoding attributes, and addition of administrative areas (i.e. Census, neighborhoods, police districts, etc.).

Data Usage: For directions on downloading and using open data please visit our How-to Guide: https://data.cincinnati-oh.gov/dataset/Open-Data-How-To-Guide/gdr9-g3ad

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

This CD-ROM (Compact Disk - Read Only Memory) contains sidescan sonar, high-resolution seismic-reflection, bathymetric, textural, and bibliographic data and interpretations collected, compiled, and produced through the U.S. Geological Survey/State of Connecticut Cooperative and the Long Island Sound Environmental Studies Project of the Coastal and Marine Geology Program, U.S. Geological Survey during October 1991 to August 1998. Cooperative research with the State of Connecticut was initiated in 1982. During the initial phase of this cooperative program, geologic framework studies in Long Island Sound were completed. The second and current phase of the program, which is the focus of this CD-ROM, emphasizes studies of sediment distribution, processes that control sediment distribution, near-shore environmental concerns, and the relationship of benthic communities to sea-floor geology. The study area covers all of Long Island Sound, which is bordered on the north by the rocky shoreline of Connecticut, on the east by Block Island Sound, on the south by the eroding sandy bluffs of Long Island, and on the west by the East River and the New York metropolitan area. Sidescan sonar data were variously collected with 100 kHz Klein, Datasonics, and Edgetech systems under two survey schemes. In the first scheme, the data were collected along closely-spaced grids where the ship tracks were spaced 150 m apart and the sonar system was set to sweep 100 m to either side of the ship's track. This scheme produced the continuous-coverage acoustic images that are stored on the CD-ROM as TIF files. In the second scheme, the sidescan sonar data collected along reconnaissance lines spaced about 2,400 m apart. Only selected portions of this data, when used for geologic interpretation, are stored on this CD-ROM. Under both survey schemes, the sidescan sonar data were processed according to procedures summarized by Danforth and others (1991) and Paskevich (1992a, 1992b, 1992c). The seismic reflection data were variously collected with an Ocean Research Equipment 3.5-kHz profiler transmitting at a 0.25-s repetition rate and a Datasonics CHIRP system set to sweep between 2-7 kHz. Only selected seismic-reflection data, which are used as examples in geologic interpretations, are stored as GIF-formatted images on this CD-ROM. Navigation during this project was determined with a differential Global Positioning System (GPS); position data were logged at 10-second intervals. The bathymetric data were collected by means of a 200-kHz echo sounder and logged digitally. Surficial sediment (0-2 cm below the sediment-water interface) sampling completed as part of this project was conducted using a Van Veen grab sampler equipped with an Osprey video and still camera system. The photographic system was used to appraise bottom variability around stations, faunal communities, and sedimentary processes. It also documented bedrock outcrops and boulder fields where samples could not be collected. The fine fraction (less than 62 microns) was analyzed by Coulter Counter (Shideler, 1976); the coarse fraction was analyzed by sieving (gravel) and by rapid sediment analyzer (sand; Schlee, 1966). The data were corrected for the salt content of interstitial water. Size classifications are based on the method proposed by Wentworth (1929) and were calculated using the inclusive graphics statistical method (Folk, 1974), using the nomenclature proposed by Shepard (1954). A detailed discussion of the sedimentological methods employed are given in Poppe and others (1985); a detailed description of the methods used to perform the CHN analyses are given in Poppe and others (1996) . The database presented here contains over 14,000 records and 83 fields (see the Data Dictionary below). The specific fields and parameters have been chosen based on the data produced by the sedimentation laboratory of the Coastal and Marine Geology Program of the U.S. Geological Survey in Woods Hole, Mass., and the format of information typically found in the literature. Because the data have come from numerous sources, there are differing amounts and types of information. Most of the samples or sets of samples do not have data in all of the given fields. However, additional fields, qualifiers, and data can be added in virtually unlimited fashion to accommodate specific needs. The database itself is provided in four formats: Microsoft EXCEL, ver. 5, Quattro Pro for Windows, Dbase IV, and Tab-delimited ASCII text. Four bathymetric data sets are presented and include:1) Interpretations of the bathymetry within the continuous-coverage sidescan sonar study areas; 2} The NOS database modified to remove extraneous data (i.e. bouys); 3) Contoured National Ocean Service (NOS) bathymetry digitized by Applied Geographics Inc., Boston, Massachusetts; and 4) a fly-by based on the modified NOS database. Data files are present in ASCII format with navigation and depth in meters. The bathymetric interpretations within the sidescan sonar study areas are based on mean sea level and stored as TIF images; the NOS data are based on mean low sea level; and the fly-by is configured to run in QuickTime or MPEG, which can be downloaded from this CD-ROM. The bibliographic database, which contains over 2,000 references, is stored as an ASCII text, Microsoft Word, Corel WordPerfect, HTML, and Microsoft EXCEL files. This bibliography is largely a compilation of references from Lewis and Coffin (1985) and the GENCAT bibliographic database at the Long Island Sound Resource Center, Connecticut Department of Environmental Protection, Groton, Connecticut. These sources have been supplemented by citations from the BIOSIS, GEOREF, and FISH AND FISHERIES WORLDWIDE bibliographic databases.

Steam Dataset 2025: Multi-Modal Gaming Analytics Platform

The first multi-modal Steam dataset with semantic search capabilities. 239,664 applications collected from official Steam Web APIs with PostgreSQL database architecture, vector embeddings for content discovery, and comprehensive review analytics.

Made by a lifelong gamer for the gamer in all of us. Enjoy!🎮

GitHub Repository https://github.com/vintagedon/steam-dataset-2025

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F28514182%2F4b7eb73ac0f2c3cc9f0d57f37321b38f%2FScreenshot%202025-10-18%20180450.png?generation=1760825194507387&alt=media" alt=""> 1024-dimensional game embeddings projected to 2D via UMAP reveal natural genre clustering in semantic space

What Makes This Different

Unlike traditional flat-file Steam datasets, this is built as an analytically-native database optimized for advanced data science workflows:

☑️ Semantic Search Ready - 1024-dimensional BGE-M3 embeddings enable content-based game discovery beyond keyword matching

☑️ Multi-Modal Architecture - PostgreSQL + JSONB + pgvector in unified database structure

☑️ Production Scale - 239K applications vs typical 6K-27K in existing datasets

☑️ Complete Review Corpus - 1,048,148 user reviews with sentiment and metadata

☑️ 28-Year Coverage - Platform evolution from 1997-2025

☑️ Publisher Networks - Developer and publisher relationship data for graph analysis

☑️ Complete Methodology & Infrastructure - Full work logs document every technical decision and challenge encountered, while my API collection scripts, database schemas, and processing pipelines enable you to update the dataset, fork it for customized analysis, learn from real-world data engineering workflows, or critique and improve the methodology

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F28514182%2F649e9f7f46c6ce213101d0948c89e8ac%2F4_price_distribution_by_top_10_genres.png?generation=1760824835918620&alt=media" alt=""> Market segmentation and pricing strategy analysis across top 10 genres

What's Included

Core Data (CSV Exports): - 239,664 Steam applications with complete metadata - 1,048,148 user reviews with scores and statistics - 13 normalized relational tables for pandas/SQL workflows - Genre classifications, pricing history, platform support - Hardware requirements (min/recommended specs) - Developer and publisher portfolios

Advanced Features (PostgreSQL): - Full database dump with optimized indexes - JSONB storage preserving complete API responses - Materialized columns for sub-second query performance - Vector embeddings table (pgvector-ready)

Documentation: - Complete data dictionary with field specifications - Database schema documentation - Collection methodology and validation reports

Example Analysis: Published Notebooks (v1.0)

Three comprehensive analysis notebooks demonstrate dataset capabilities. All notebooks render directly on GitHub with full visualizations and output:

📊 Platform Evolution & Market Landscape

View on GitHub | PDF Export
28 years of Steam's growth, genre evolution, and pricing strategies.

🔍 Semantic Game Discovery

View on GitHub | PDF Export
Content-based recommendations using vector embeddings across genre boundaries.

🎯 The Semantic Fingerprint

View on GitHub | PDF Export
Genre prediction from game descriptions - demonstrates text analysis capabilities.

Notebooks render with full output on GitHub. Kaggle-native versions planned for v1.1 release. CSV data exports included in dataset for immediate analysis.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F28514182%2F4079e43559d0068af00a48e2c31f0f1d%2FScreenshot%202025-10-18%20180214.png?generation=1760824950649726&alt=media" alt=""> *Steam platfor...

The GlobalPhone pronunciation dictionaries, created within the framework of the multilingual speech and language corpus GlobalPhone, were developed in collaboration with the Karlsruhe Institute of Technology (KIT).

The GlobalPhone pronunciation dictionaries contain the pronunciations of all word forms found in the transcription data of the GlobalPhone speech & text database. The pronunciation dictionaries are currently available in 20 languages: Arabic (29230 entries/27059 words), Bulgarian (20193 entries), Chinese-Mandarin (73388 pronunciations), Croatian (23497 entries/20628 words), Czech (33049 entries/32942 words), French (36837 entries/20710 words), German (48979 entries/46035 words), Hausa (42662 entries/42079 words), Japanese (18094 entries), Korean (3500 entries syllable-based, 97493 entries/81602 words word-based), Polish (36484 entries), Portuguese (Brazilian) (58803 entries/58787 words), Russian (28818 entries/27667 words), Spanish (Latin American) (43264 entries/33960 words), Swahili (10664 entries), Swedish (25401 entries/25356 words), Thai (small set with 12420 entries and larger set with 25570 entries/22462 words), Turkish (31330 entries/31087 words), Ukrainian (7748 entries/7740 words), and Vietnamese (38504 entries/29974 words).

1) Dictionary Encoding: The GlobalPhone pronunciation dictionary entries consist of full word forms and are either given in the original script of that language, mostly in UTF-8 encoding (Bulgarian, Chinese-Mandarin, Croatian, Czech, French, Japanese, Korean, Polish, Portuguese, Russian, Spanish, Swahili, Turkish, Thai, Ukrainian, Vietnamese) corresponding to the trl-files of the GlobalPhone transcriptions or in a Romanized versions encoded in ASCII/ISO-8859 encoding to fit the rmn-files of the GlobalPhone transcriptions (Arabic, German, Hausa (simplified boko), Swedish). In some languages both versions exist. Romanization was performed by reversible mappings, which are documented in most cases. Furthermore, in several languages, alternative versions are available, e.g. Chinese-Mandarin is provided in both, UTF-8 for Hanzi character-based dictionary (trl) and Pinyin version in ASCII (rmn); Korean is provided in both, UTF-8 for Eojeol- and Hangul-based dictionary (trl) and ASCII for a Romanized version in which a data-driven algorithm was performed to merge syllable units into a reasonable set of word-like units (rmn).

2) Dictionary Phone set: The phone sets for each language were derived individually from the literature following best practices for automatic speech processing. Each phone set is explained and described in the documentation using the international standards of the International Phonetic Alphabet (IPA). A language independent GlobalPhone naming convention for the phone sets is used (indicated by “M_”) to support the sharing of phones across languages to build multilingual pronunciation dictionaries or acoustic models. For historical reasons, some dictionaries still use language dependent phone names. For most of those dictionaries, the documentation provides a mapping to the GlobalPhone phone names.

3) Dictionary Generation: Whenever the grapheme-to-phoneme relationship allowed, the dictionaries were created semi-automatically. In the first step handcrafted grapheme-to-phoneme rules were applied to generate initial pronunciations from all word forms appearing in the GlobalPhone transcriptions. The number of rules highly depends on the language. In the second step, the generated pronunciations were manually checked by native speakers, correcting potential errors of the automatic pronunciation generation process. In the third step, most of the dictionaries were enriched by special entries such as acronyms, foreign words, pronunciation variants, numbers, or partial words and cross-checked by the native speakers. Most of the dictionaries have been applied to large vocabulary speech recognition. In many cases the GlobalPhone dictionaries were compared to straight-forward grapheme-based speech recognition and to alternative sources, such as Wiktionary and usually demonstrated to be superior in terms of quality, coverage, and accuracy.

4) Format: The format of the dictionaries is the same across languages and is straight-forward. Each line consists of one word form and its pronunciation separated by blank. The pronunciation consists of a concatenation of phone symbols separated by blanks. Both, words and their pronunciations are given in tcl-script list format, i.e. enclosed in “{}”, since phones can carry tags, indicating the tone, length or stress of a vowel or the palatalization of consonants, or the word boundary tag “WB”, indicating the boundary of a dictionary unit. The WB tag can for example be included as a standard question in the decision tree questions for capturing crossword models in context-dependent modeling. Pronunciation variants are indicated by (

5) Documentation: The pronunciation dictionaries for each language are complemented by a documentation that describes the format of the dictionary, the phone set including its mapping to the International Phonetic Alphabet (IPA), and the frequency distribution of the phones in the dictionary. Most of the pronunciation dictionaries have been successfully applied to large vocabulary speech recognition. Experimental results and general information about the GlobalPhone corpus were published widely in conference or journal papers and partially referenced in the documentation.

A good summary of the pronunciation dictionaries is provided in: Tanja Schultz and Tim Schlippe (2014) GlobalPhone: Pronunciation Dictionaries in 20 Languages, Proceedings of the 9th edition of the Language Resources and Evaluation Conference (LREC), Reykjavik, Iceland, 2014.

Context

WordNet Thesaurus with the various files in CSV Format. You can use the definitions in any commercial project, but you must include the license below (link to the license page also provided). The CSV files I created for these WordNet entries contain each Word, Character Count, Definition, Part of Speech (where available) and Examples/Terms (where available).

Content

This collection of the WordNet Thesaurus includes the following CSV files:

1. Adjectives
2. Adverbs
3. Antonyms
4. Hypernyms
5. Hyponyms
6. Nouns
7. Synonyms
8. Thesaurus
9. Verbs

Acknowledgements

Here is the WordNet license, in their own words, as obtained on this page (SEE THE LICENSE PAGE HERE):

WordNet Release 3.0 This software and database is being provided to you, the LICENSEE, by Princeton University under the following license. By obtaining, using and/or copying this software and database, you agree that you have read, understood, and will comply with these terms and conditions.: Permission to use, copy, modify and distribute this software and database and its documentation for any purpose and without fee or royalty is hereby granted, provided that you agree to comply with the following copyright notice and statements, including the disclaimer, and that the same appear on ALL copies of the software, database and documentation, including modifications that you make for internal use or for distribution. WordNet 3.0 Copyright 2006 by Princeton University. All rights reserved. THIS SOFTWARE AND DATABASE IS PROVIDED "AS IS" AND PRINCETON UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES, EXPRESS OR IMPLIED. BY WAY OF EXAMPLE, BUT NOT LIMITATION, PRINCETON UNIVERSITY MAKES NO REPRESENTATIONS OR WARRANTIES OF MERCHANT- ABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OR THAT THE USE OF THE LICENSED SOFTWARE, DATABASE OR DOCUMENTATION WILL NOT INFRINGE ANY THIRD PARTY PATENTS, COPYRIGHTS, TRADEMARKS OR OTHER RIGHTS. The name of Princeton University or Princeton may not be used in advertising or publicity pertaining to distribution of the software and/or database. Title to copyright in this software, database and any associated documentation shall at all times remain with Princeton University and LICENSEE agrees to preserve same.

Of interest to pharmaceutical, nutritional, and biomedical researchers, as well as individuals and companies involved with alternative therapies and and herbal products, this database is one of the world's leading repositories of ethnobotanical data, evolving out of the extensive compilations by the former Chief of USDA's Economic Botany Laboratory in the Agricultural Research Service in Beltsville, Maryland, in particular his popular Handbook of phytochemical constituents of GRAS herbs and other economic plants (CRC Press, Boca Raton, FL, 1992). In addition to Duke's own publications, the database documents phytochemical information and quantitative data collected over many years through research results presented at meetings and symposia, and findings from the published scientific literature. The current Phytochemical and Ethnobotanical databases facilitate plant, chemical, bioactivity, and ethnobotany searches. A large number of plants and their chemical profiles are covered, and data are structured to support browsing and searching in several user-focused ways. For example, users can

get a list of chemicals and activities for a specific plant of interest, using either its scientific or common name download a list of chemicals and their known activities in PDF or spreadsheet form find plants with chemicals known for a specific biological activity display a list of chemicals with their LD toxicity data find plants with potential cancer-preventing activity display a list of plants for a given ethnobotanical use find out which plants have the highest levels of a specific chemical

References to the supporting scientific publications are provided for each specific result. Resources in this dataset:Resource Title: Duke-Source-CSV.zip. File Name: Duke-Source-CSV.zipResource Description: Dr. Duke's Phytochemistry and Ethnobotany - raw database tables for archival purposes. Visit https://phytochem.nal.usda.gov/phytochem/search for the interactive web version of the database.Resource Title: Data Dictionary (preliminary). File Name: DrDukesDatabaseDataDictionary-prelim.csvResource Description: This Data Dictionary describes the columns for each table. [Note that this is in progress and some variables are yet to be defined or are unused in the current implementation. Please send comments/suggestions to nal-adc-curator@ars.usda.gov ]

The National Mortgage Database (NMDB®) is a nationally​​ representative five percent sample of residential mortgages in the United States. Publication ​of aggregate data from NMDB is a step toward implementing the statutory requirements of section 1324(c) of the Federal Housing Enterprises Financial Safety and Soundness Act of 1992, as amended by the Housing and Economic Recovery Act of 2008. The statute requires FHFA to conduct a monthly mortgage market survey to collect data on the characteristics of individual mortgages, both Enterprise and​ non-Enterprise, and to make the data a​vailable to the public while protecting the privacy of the borrowers.​Notes:1) All CSV file headers are now standardized as described in the Data Dictionary and Technical Notes and all CSV files are zipped.2) Alternate wide format CSV files are available. The wide format may be more easily opened by MS Excel.

This repository contains two datasets for our recent work "Learning Properties of Ordered and Disordered Materials from Multi-fidelity Data". The first data set is a multi-fidelity band gap data for crystals, and the second data set is the molecular energy data set for molecules.1. Multi-fidelity band gap data for crystalsThe full band gap data used in the paper is located at band_gap_no_structs.gz. Users can use the following code to extract it. import gzipimport jsonwith gzip.open("band_gap_no_structs.gz", "rb") as f: data = json.loads(f.read())data is a dictionary with the following format{"pbe": {mp_id: PBE band gap, mp_id: PBE band gap, ...},"hse": {mp_id: HSE band gap, mp_id: HSE band gap, ...},"gllb-sc": {mp_id: GLLB-SC band gap, mp_id: GLLB-SC band gap, ...},"scan": {mp_id: SCAN band gap, mp_id: SCAN band gap, ...},"ordered_exp": {icsd_id: Exp band gap, icsd_id: Exp band gap, ...},"disordered_exp": {icsd_id: Exp band gap, icsd_id: Exp band gap, ...}}where mp_id is the Materials Project materials ID for the material, and icsd_id is the ICSD materials ID. For example, the PBE band gap of NaCl (mp-22862, band gap 5.003 eV) can be accessed by data['pbe']['mp-22862']. Note that the Materials Project database is evolving with time and it is possible that certain ID is removed in latest release and there may also be some band gap value change for the same material. To get the structure that corresponds to the specific material id in Materials Project, users can use the pymatgen REST API. 1.1. Register at Materials Project https://www.materialsproject.org and get an API key.1.2. In python, do the following to get the corresponding computational structure. from pymatgen import MPRester mpr = MPRester(#Your API Key) structure = mpr.get_structure_by_material_id(#mp_id)A dump of all the material ids and structures for 2019.04.01 MP version is provided here: https://ndownloader.figshare.com/files/15108200. Users can download the file and extract the material_id and structure from this file for all materials. The structure in this case is a cif file. Users can use again pymatgen to read the cif string and get the structure. from pymatgen.core import Structurestructure = Structure.from_str(#cif_string, fmt='cif')For the ICSD structures, the users are required to have commercial ICSD access. Hence the structures will not be provided here.2. Multi-fidelity molecular energy dataThe molecule_data.zip contains two datasets in json format. 2.1 G4MP2.json contains two fidelity G4MP2 (6095) and B3LYP (130831) calculations results on QM9 molecules {"G4MP2": {"U0": {ID: G4MP2 energy (eV), ...}, { "molecules": {ID: Pymatgen molecule dict, ...}},"B3LYP": {"U0": {ID: B3LYP energy (eV), ...} {"molecules": {ID: Pymatgen molecule dict, ...}}}2.2 qm7b.json contains the molecule energy calculation resultsi for 7211 molecules using HF, MP2 and CCSD(T) methods with 6-31g, sto-3g and cc-pvdz bases. {"molecules": {ID: Pymatgen molecule dict, ...},"targets": {ID: {"HF": {"sto3g": Atomization energy (kcal/mol), "631g": Atomization energy (kcal/mol), "cc-pvdz": Atomization energy (kcal/mol)}, "MP2": {"sto3g": Atomization energy (kcal/mol), "631g": Atomization energy (kcal/mol), "cc-pvdz": Atomization energy (kcal/mol)}, "CCSD(T)": {"sto3g": Atomization energy (kcal/mol), "631g": Atomization energy (kcal/mol), "cc-pvdz": Atomization energy (kcal/mol)}, ...}}}

The dataset, Survey-SR, provides the nutrient data for assessing dietary intakes from the national survey What We Eat In America, National Health and Nutrition Examination Survey (WWEIA, NHANES). Historically, USDA databases have been used for national nutrition monitoring (1). Currently, the Food and Nutrient Database for Dietary Studies (FNDDS) (2), is used by Food Surveys Research Group, ARS, to process dietary intake data from WWEIA, NHANES. Nutrient values for FNDDS are based on Survey-SR. Survey-SR was referred to as the "Primary Data Set" in older publications. Early versions of the dataset were composed mainly of commodity-type items such as wheat flour, sugar, milk, etc. However, with increased consumption of commercial processed and restaurant foods and changes in how national nutrition monitoring data are used (1), many commercial processed and restaurant items have been added to Survey-SR. The current version, Survey-SR 2013-2014, is mainly based on the USDA National Nutrient Database for Standard Reference (SR) 28 (2) and contains sixty-six nutrientseach for 3,404 foods. These nutrient data will be used for assessing intake data from WWEIA, NHANES 2013-2014. Nutrient profiles were added for 265 new foods and updated for about 500 foods from the version used for the previous survey (WWEIA, NHANES 2011-12). New foods added include mainly commercially processed foods such as several gluten-free products, milk substitutes, sauces and condiments such as sriracha, pesto and wasabi, Greek yogurt, breakfast cereals, low-sodium meat products, whole grain pastas and baked products, and several beverages including bottled tea and coffee, coconut water, malt beverages, hard cider, fruit-flavored drinks, fortified fruit juices and fruit and/or vegetable smoothies. Several school lunch pizzas and chicken products, fast-food sandwiches, and new beef cuts were also added, as they are now reported more frequently by survey respondents. Nutrient profiles were updated for several commonly consumed foods such as cheddar, mozzarella and American cheese, ground beef, butter, and catsup. The changes in nutrient values may be due to reformulations in products, changes in the market shares of brands, or more accurate data. Examples of more accurate data include analytical data, market share data, and data from a nationally representative sample. Resources in this dataset:Resource Title: USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES 2013-14 (Survey SR 2013-14). File Name: SurveySR_2013_14 (1).zipResource Description: Access database downloaded on November 16, 2017. US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. USDA National Nutrient Database for Standard Reference Dataset for What We Eat In America, NHANES (Survey-SR), October 2015. Resource Title: Data Dictionary. File Name: SurveySR_DD.pdf