The National Flood Hazard Layer (NFHL) data incorporates all Digital Flood Insurance Rate Map(DFIRM) databases published by FEMA, and any Letters Of Map Revision (LOMRs) that have been issued against those databases since their publication date. The DFIRM Database is the digital, geospatial version of the flood hazard information shown on the published paper Flood Insurance Rate Maps(FIRMs). The primary risk classifications used are the 1-percent-annual-chance flood event, the 0.2-percent-annual-chance flood event, and areas of minimal flood risk. The NFHL data are derived from Flood Insurance Studies (FISs), previously published Flood Insurance Rate Maps (FIRMs), flood hazard analyses performed in support of the FISs and FIRMs, and new mapping data where available. The FISs and FIRMs are published by the Federal Emergency Management Agency (FEMA). The specifications for the horizontal control of DFIRM data are consistent with those required for mapping at a scale of 1:12,000. The NFHL data contain layers in the Standard DFIRM datasets except for S_Label_Pt and S_Label_Ld. The NFHL is available as State or US Territory data sets. Each State or Territory data set consists of all DFIRMs and corresponding LOMRs available on the publication date of the data set.

Small business benchmarks are a guide to help you compare your business's performance against similar businesses in the same industry.

For more info see: https://www.ato.gov.au/Business/Small-business-benchmarks/

The NIST Computational Chemistry Comparison and Benchmark Database is a collection of experimental and ab initio thermochemical properties for a selected set of gas-phase molecules. The goals are to provide a benchmark set of experimental data for the evaluation of ab initio computational methods and allow the comparison between different ab initio computational methods for the prediction of gas-phase thermochemical properties. The data files linked to this record are a subset of the experimental data present in the CCCBDB.

The Chicago Building Energy Use Benchmarking Ordinance calls on existing municipal, commercial, and residential buildings larger than 50,000 square feet to track whole-building energy use, report to the City annually, and verify data accuracy every three years. The law, which was phased in from 2014-2017, covers less than 1% of Chicago’s buildings, which account for approximately 20% of total energy used by all buildings. For more details, including ordinance text, rules and regulations, and timing, please visit www.CityofChicago.org/EnergyBenchmarking

The ordinance authorizes the City to share property-specific information with the public, beginning with the second year in which a building is required to comply.

The dataset represents self-reported and publicly-available property information by calendar year. Please note that the "Data Year" column refers to the year to which the data apply, not the year in which they were reported. That column and filtered views under "Related Content" can be used to isolate specific years.

Agentic Data Access Benchmark (ADAB)

Agentic Data Access Benchmark is a set of real-world questions over few "closed domains" to illustrate the evaluation of closed domain AI assistants/agents. Closed domains are domains where data is not available implicitly in the LLM as they reside in secure or private systems e.g. enterprise databases, SaaS applications, etc and AI solutions require mechanisms to connect an LLM to such data. If you are evaluating an AI product or building… See the full description on the dataset page: https://huggingface.co/datasets/hasura/agentic-data-access-benchmark.

The ont-open-data registry provides reference sequencing data from Oxford Nanopore Technologies to support, 1) Exploration of the characteristics of nanopore sequence data. 2) Assessment and reproduction of performance benchmarks 3) Development of tools and methods. The data deposited showcases DNA sequences from a representative subset of sequencing chemistries. The datasets correspond to publicly-available reference samples (e.g. Genome In A Bottle reference cell lines). Raw data are provided with metadata and scripts to describe sample and data provenance.

This repository contains the underlying data from benchmark experiments for Drifting Acoustic Instrumentation SYstems (DAISYs) in waves and currents described in "Performance of a Drifting Acoustic Instrumentation SYstem (DAISY) for Characterizing Radiated Noise from Marine Energy Converters" (https://link.springer.com/article/10.1007/s40722-024-00358-6). DAISYs consist of a surface expression connected to a hydrophone recording package by a tether. Both elements are instrumented to provide metadata (e.g., position, orientation, and depth). Information about how to build DAISYs is available at https://www.pmec.us/research-projects/daisy. The repository's primary content is three compressed archives (.zip format), each containing multiple MATLAB binary data files (.mat format). A table relating individual data files to figures in the paper, as well as the structure of each file, is included in the repository as a Word document (Data Description MHK-DR.docx). Most of the files contain time series information for a single DAISY deployment (file naming convention: [site]DAISY[Drift #].mat) consisting of processed hydrophone data and associated metadata. For a limited number of DAISY deployments, the hydrophone package was replaced with an acoustic Doppler velocimeter (file naming convention: [site]DAISY[Drift #]_ADV.mat). Data were collected over several years at three locations: (1) Sequim Bay at Pacific Northwest National Laboratory's Marine & Coastal Research Laboratory (MCRL) in Sequim, WA, the energetic tidal channel in Admiralty Inlet, WA (Admiralty Inlet), and the U.S. Navy's Wave Energy Test Site (WETS) in Kaneohe, HI. Brief descriptions of data files at each location follow. MCRL - (1) Drift #4 and #16 contrast the performance of a DAISY and a reference hydrophone (icListen HF Reson), respectively, in the quiescent interior of Sequim Bay (September 2020). (2) Drift #152 and #153 are velocity measurements for a drifting acoustic Doppler velocimeter in in the tidally-energetic entrance channel inside a flow shield and exposed to the flow, respectively (January 2018). (3) Two non-standard files are also included: DAISY_data.mat corresponds to a subset of a DAISY drift over an Adaptable Monitoring Package (AMP) and AMP_data.mat corresponds to approximately co-temporal data for a stationary hydrophone on the AMP (February 2019). Admiralty Inlet - (1) Drift #1-12 correspond to tests with flow shielded DAISYs, unshielded DAISYs, a reference hydrophone, and drifting acoustic Doppler velocimeter with 5, 10, and 15 m tether lengths between surface expression and hydrophone recording package (July 2022). (2) Drift #13-20 correspond to tests of flow shielded DAISYs with three different tether materials (rubber cord, nylon line, and faired nylon line) in lengths of 5, 10, and 15 m (July 2022). WETS - (1) Drift #30-32 correspond to tests with a heave plate incorporated into the tether (standard configuration for wave sites), rubber cord only, and rubber cord, but with a flow shielded hydrophone (November 2022). (2) Drift #49-58 and Drift #65-68 correspond to measurements around mooring infrastructure at the 60 m berth where time-delay-of-arrival localization was demonstrated for different DAISY arrangements and hydrophone depths (November 2022).

This dataset represents a newer version of the NQUADS files in RDF from Publication Offices used for benchmarking graph databases.

We are releasing the tracing dataset of four microservice benchmarks deployed on our dedicated Kubernetes cluster consisting of 15 heterogeneous nodes. The dataset is not sampled and is from selected types of requests in each benchmark, i.e., compose-posts in the social network application, compose-reviews in the media service application, book-rooms in the hotel reservation application, and reserve-tickets in the train ticket booking application. The four microservice applications come from DeathStarBench and Train-Ticket. The performance anomaly injector is from FIRM. The dataset was preprocessed from the raw data generated in FIRM's tracing system. The dataset is separated by on which microservice component is the performance anomaly located (as the file name suggests). Each dataset is in CSV format and fields are separated by commas. Each line consists of the tracing ID and the duration (in 10^(-3) ms) of each component. Execution paths are specified in execution_paths.txt in each directory.

These are locations that are to be used as an elevation reference and contain the official elevation and last known latitude and longitude.

The Aquatic Life Benchmarks is an EPA-developed set of criteria for freshwater species. These benchmarks are based on toxicity values reviewed by EPA and used in the Agency's risk assessments developed as part of the decision-making process for pesticide registration.

The size and share of the market is categorized based on Type (Cloud, SaaS, Web, On Premise) and Application (Technology & IT, Financial Services, Consumer & Retail, Government, Healthcare, Manufacturing, Other Industry) and geographical regions (North America, Europe, Asia-Pacific, South America, and Middle-East and Africa).

The BuildingsBench datasets consist of: Buildings-900K: A large-scale dataset of 900K buildings for pretraining models on the task of short-term load forecasting (STLF). Buildings-900K is statistically representative of the entire U.S. building stock. 7 real residential and commercial building datasets for benchmarking two downstream tasks evaluating generalization: zero-shot STLF and transfer learning for STLF. Buildings-900K can be used for pretraining models on day-ahead STLF for residential and commercial buildings. The specific gap it fills is the lack of large-scale and diverse time series datasets of sufficient size for studying pretraining and finetuning with scalable machine learning models. Buildings-900K consists of synthetically generated energy consumption time series. It is derived from the NREL End-Use Load Profiles (EULP) dataset (see link to this database in the links further below). However, the EULP was not originally developed for the purpose of STLF. Rather, it was developed to "...help electric utilities, grid operators, manufacturers, government entities, and research organizations make critical decisions about prioritizing research and development, utility resource and distribution system planning, and state and local energy planning and regulation." Similar to the EULP, Buildings-900K is a collection of Parquet files and it follows nearly the same Parquet dataset organization as the EULP. As it only contains a single energy consumption time series per building, it is much smaller (~110 GB). BuildingsBench also provides an evaluation benchmark that is a collection of various open source residential and commercial real building energy consumption datasets. The evaluation datasets, which are provided alongside Buildings-900K below, are collections of CSV files which contain annual energy consumption. The size of the evaluation datasets altogether is less than 1GB, and they are listed out below: ElectricityLoadDiagrams20112014 Building Data Genome Project-2 Individual household electric power consumption (Sceaux)

Investments in infrastructure have been on the development agenda of Latin American and Caribbean (LCR) countries as they move towards economic and social progress. Investing in infrastructure is investing in human welfare by providing access to and quality basic infrastructure services. Improving the performance of the electricity sector is one such major infrastructure initiative and the focus of this benchmarking data. A key initiative for both public and private owned distribution utilities has been to upgrade their efficiency as well as to increase the coverage and quality of service. In order to accomplish this goal, this initiative serves as a clearing house for information regarding the country and utility level performance of electricity distribution sector. This initiative allows countries and utilities to benchmark their performance in relation to other comparator utilities and countries. In doing so, this benchmarking data contributes to the improvement of the electricity sector by filling in knowledge gaps for the identification of the best performers (and practices) of the region. This benchmarking database consists of detailed information of 25 countries and 249 utilities in the region. The data collected for this benchmarking project is representative of 88 percent of the electrification in the region. Through in-house and field data collection, consultants compiled data based on accomplishments in output, coverage, input, labor productivity, operating performance, the quality of service, prices, and ownership. By serving as a mirror of good performance, the report allows for a comparative analysis and the ranking of utilities and countries according to the indicators used to measure performance. Although significant efforts have been made to ensure data comparability and consistency across time and utilities, the World Bank and the ESMAP do not guarantee the accuracy of the data included in this work. Acknowledgement: This benchmarking database was prepared by a core team consisting of Luis Alberto Andres (Co-Task Team Leader), Jose Luis Guasch (Co-Task Team Leader), Julio A. Gonzalez, Georgeta Dragoiu, and Natalie Giannelli. The team was benefited by data contributions from Jordan Z. Schwartz (Senior Infrastructure Specialist, LCSTR), Lucio Monari (Lead Energy Economist, LCSEG), Katharina B. Gassner (Senior Economist, FEU), and Martin Rossi (consultant). Funding was provided by the Energy Sector Management Assistance Program (ESMAP) and the World Bank. Comments and suggestion are welcome by contacting Luis Andres (landres@worldbank.org)

OSNI Benchmark data consists of point data giving the location of the points, along with the associated text which shows the height value. Bench marks are shown in metres above mean sea level. This data is not maintained. We recommend using a Global Navigation Satellite System (GNSS) and the latest geoid model to find orthometric height in Northern Ireland. Published here for OpenData. By download or use of this dataset you agree to abide by the LPS Open Government Data License.Please Note for Open Data NI Users: Esri Rest API is not Broken, it will not open on its own in a Web Browser but can be copied and used in Desktop and Webmaps

Annual Excel pivot tables display the statewide top 25 MS-DRGs (Medicare Severity-Diagnosis Related Groups) by Average Charge per Stay. Each California hospital can be compared to the statewide benchmarks for those same MS-DRGs.

The Clean and Affordable Energy Act of 2008 established that all private buildings over 50,000 gross square feet within the District of Columbia, including multifamily residences, must annually measure and disclose their energy and water consumption to the Department of Energy and Environment (DOEE). Benchmarking is defined as tracking a building’s energy and water use and using a standard metric to compare the building’s performance against past performance and to its peers nationwide. These comparisons have been shown to drive energy efficiency upgrades and increase occupancy rates and property values. The District of Columbia has chosen U.S. EPA’s free, industry-standard ENERGY STAR® Portfolio Manager® tool for benchmarking and reporting. DDOE is required to publicly disclose the ENERGY STAR® Benchmarking results for each publicly or privately owned building that is subject to the benchmarking law, beginning with the 2nd year of benchmarking data for that building. For more information, see https://doee.dc.gov/energybenchmarking.

The Datum of the majority of the City of Sacramento benchmarks is the National Geodetic Vertical Datum of 1929 (NGVD 29) and was based upon a plane datum with reference to four U.S. Government monuments. Some North American Vertical Datum 1988 (NAVD 88) differential baselines have been conducted and the elevations of those benchmarks have been included and identified in this database.VERTICAL CONTROL DISCLAIMER AND GENERAL USE DISCLAIMER City of Sacramento Vertical Control Network, also known as “City of Sacramento Datum” information, is furnished by the Department of Public Works - Engineering Services. It was developed and collected for the purpose of establishing reference points for all survey activities within Sacramento City limits. City of Sacramento makes no warranties, expressed or implied, concerning the accuracy, completeness, reliability, or suitability of this data for any other particular use. Furthermore, the City of Sacramento assumes not liability for any errors, omissions, or inaccuracies associated with the use or misuse of such data. The City of Sacramento tries to keep this information current and accurate.If you find a missing or destroyed City of Sacramento benchmark please contact the City Land Surveyor by calling (916) 808-8777.

ACCEPTABLE USE The City of Sacramento Datum (unadjusted NGVD29) local benchmarks provided here are acceptable as City of Sacramento benchmarks for the purpose of establishing and extending vertical control to design surveys. Reference Sacramento City Code, §1.12.010.All information compiled on this website is provided as a public service and for general informational purposes only. In preparation of these pages, every effort has been made by the Department of Public Works - Engineering Services to offer the most current, correct and concise information possible. The City of Sacramento and its authorized agents and contractors disclaim any responsibility for typographical errors and accuracy of the information that may be contained on the City of Sacramento website; www.cityofsacramento.orgBy accessing the information, data, materials and links contained in the City of Sacramento World Wide Web pages, you hereby agree to and accept the following terms and conditions: The Department of Public Works - Engineering Services shall not be liable for the improper or incorrect use of data, information, materials, links or related graphics described and /or contained herein. The Department of Public Works - Engineering Services shall not be liable for any demand claim, regardless of form or action, arising out of or incident to the posting of information or data on this website; the accessing or use of any information or data on this website; and/or the acts or omissions of any person or entity accessing or using any information from this website.The user hereby recognizes that the information, data, materials and related graphics are dynamic and may change over time without notice. The Department of Public Works - Engineering Services is not responsible for the use or reliance upon this information. There are links and pointers to third party internet websites contained in the City of Sacramento website. These sites linked from the City of Sacramento website are not under the City’s control. The City of Sacramento and its authorized agents and contractors do not assume any responsibility or liability for any information, communications or materials available at such linked sites, or at any link contained in a liked site. Each individual site has its own set of policies about what information is appropriate for public access. User assumes sole responsibility for use of third party links and pointers.

Benchmark is a Point FeatureClass representing land-surveyed benchmarks in Cupertino. Benchmarks are stable sites used to provide elevation data. It is primarily used as a reference layer. The layer is updated as needed by the GIS department. Benchmark has the following fields:

OBJECTID: Unique identifier automatically generated by Esri type: OID, length: 4, domain: none

ID: Unique identifier assigned to the Benchmark type: Integer, length: 4, domain: none

REF_MARK: The reference mark associated with the Benchmark type: String, length: 10, domain: none

ELEV: The elevation of the Benchmark type: Double, length: 8, domain: none

Shape: Field that stores geographic coordinates associated with feature type: Geometry, length: 4, domain: none

Description: A more detailed description of the Benchmark type: String, length: 200, domain: none

Owner: The owner of the Benchmark type: String, length: 10, domain: none

GlobalID: Unique identifier automatically generated for features in enterprise database type: GlobalID, length: 38, domain: none Operator:

The user responsible for updating this database type: String, length: 255, domain: OPERATOR

last_edited_date: The date the database row was last updated type: Date, length: 8, domain: none

created_date: The date the database row was initially created type: Date, length: 8, domain: none

VerticalDatum: The vertical datum associated with the Benchmarktype: String, length: 100, domain: none

Los Angeles County Department of Public Works’ Vertical Control Network is composed of more than 1,700 miles (2,720 kilometers) of level runs and comprise nearly 9,000 benchmarks. The basic accuracy of the net is reflected by an indicated field probable error of ± 0.017 feet per mile (4 mm per kilometer) of leveling as determined from conditions of closure. However, because of varying degrees of subsidence and heaving, the true datum is recovered only by obtaining substantial agreement of a number of benchmarks.For each active benchmark, a point representation was created in GIS by locating them based on their description. Parcel data, mile markers, the County Address Management System (CAMS), LARIAC aerials, oblique photos, 2-foot contour lines and/or Google Street View were used in assisting with the location.The creation of the benchmarks in GIS greatly enhances the Vertical Control Network by adding visual context with respect to their representative geospatial locations. With a glance, geospatial patterns can be observed and out-of-place benchmarks can be quickly identified and remapped to the correct location after verification.To facilitate the adjustment, indexing and distribution of adjusted values in the network, the county territory was divided into 33 quads or areas. For identification purposes, each quad was given a name (for example, “Rosemead”, “La Mirada”, “Santa Fe”, and etc.). Index maps, county maps, and other information can be accessed and downloaded on the basis of each of the quads by going to Survey Division’s Benchmark Retrieval System (https://pw.lacounty.gov/sur/benchmark). General adjustments are carried out every 5 to 10 years and the provided elevation data is expected to remain sound during this period. When a quad is adjusted, new elevations will be published and the date of the readjustment will be noted. No historical data is provided, but it can be acquired from Survey Division’s Public Records Counter or via the fee based Optional Technical Research (OTR) program. For general questions, contact:Hector Chang626-458-7038hchang@dpw.lacounty.govFor survey-related questions, contact:Charles Springstun626-320-9896cspring@dpw.lacounty.govThe following resources can be used to obtain historical benchmark data:PUBLIC RECORDS COUNTER900 S. Fremont Ave, 4th FloorAlhambra, CA 918037:00 AM to 5:00 PM Mon – ThursPhone: (626) 458-5137OPTIONAL TECHNICAL RESEARCH (OTR)7:00 AM to 5:00 PM Mon – ThursPhone: (626) 458-5131