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.

Performance comparison on the benchmark noisy database.

Data on water utilities for 151 national jurisdictions, for a range of years up to and including 2017 (year range varies greatly by country and utility) on service and utility parameters (Benchmark Database) and Tariffs for 211 juristictions (Tariffs database). Information includes cost recovery, connections, population served, financial performance, non-revenue water, residential and total supply, total production. Data can be called up by utility, by group of utility, and by comparison between utilities, including the whole (global) utility database, enabling both country and global level comparison for individual utilities. Data can be downloaded in xls format.

This repository contains the list of unbound receptors, peptides and natives that was used for PatchMAN BSA filtering paper.

It also containts the databases that are used 1) search with MASTER, 2) extraction of fragments with MASTER.

This datasets generated with the LDBC SNB Data generator.

https://github.com/ldbc/ldbc_snb_datagen

It corresponds to Scale Factors 1 and 3. They are used in the following paper:

An early look at the LDBC social network benchmark's business intelligence workload

10.1145/3210259.3210268

Comparisons of CPU time on AR face database testing with scarves.

This dataset contains two Wi-Fi databases (one for training and one for test/estimation purposes in indoor positioning applications), collected in a crowdsourced mode (i.e., via 21 different devices and different users), together with a benchmarking utility software (in Matlab and Python) to illustrate various algorithms of indoor positioning based solely on WiFi information (MAC addresses and RSS values).

The data was collected in a 4-floor university building in Tampere, Finland, during Jan-Aug 2017 and it comprises 687 training fingerprints and 3951 test or estimation fingerprints.

13.10.2017: Version 2 uploaded; the revised version contains improved readme files and improved Python SW.

The dataset and/or the associated software are to be cited as follows:

E.S. Lohan, J. Torres-Sospedra, P. Richter, H. Leppäkoski, J. Huerta, A. Cramariuc, “Crowdsourced WiFi-fingerprinting database and benchmark software for indoor positioning”, Zenodo repository, DOI 10.5281/zenodo.889798

you can see our case study results by using this built database to select components and benchmark the bridgeless buck-boost PFC converters.

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 your… See the full description on the dataset page: https://huggingface.co/datasets/hasura/agentic-data-access-benchmark.

The Benchmark Energy & Geometry Database (BEGDB) collects results of highly accurate quantum mechanics (QM) calculations of molecular structures, energies and properties. These data can serve as benchmarks for testing and parameterization of other computational methods.

This resource is the implementation in XML Schema [1] of a data model that describes the Additive Manufacturing Benchmark 2022 series data. It provides a robust set of metadata for the build processes and their resulting specimens and for measurements made on these in the context of the AM Bench 2022 project.The schema was designed to support typical science questions which users of a database with metadata about the AM Bench results might wish to pose. The metadata include identifiers assigned to build products, derived specimens, and measurements; links to relevant journal publications, documents, and illustrations; provenance of specimens such as source materials and details of the build process; measurement geometry, instruments and other configurations used in measurements; and access information to raw and processed data as well as analysis descriptions of these datasets.This data model is an abstraction of these metadata, designed using the concepts of inheritance, normalization, and reusability of an object oriented language for ease of extensibility and maintenance. It is simple to incorporate new metadata as needed.A CDCS [2] database at NIST was filled with metadata provided by the contributors to the AM Bench project. They entered values for the metadata fields for an AM Bench measurement, specimen or build process in tabular spreadsheets. These entries were translated to XML documents compliant with the schema using a set of python scripts. The generated XML documents were loaded into the database with a persistent identifier (PID) assigned by the database.[1] https://www.w3.org/XML/Schema[2] https://www.nist.gov/itl/ssd/information-systems-group/configurable-data-curation-system-cdcs/about-cdcs

Annotated Benchmark of Real-World Data for Approximate Functional Dependency Discovery

This collection consists of ten open access relations commonly used by the data management community. In addition to the relations themselves (please take note of the references to the original sources below), we added three lists in this collection that describe approximate functional dependencies found in the relations. These lists are the result of a manual annotation process performed by two independent individuals by consulting the respective schemas of the relations and identifying column combinations where one column implies another based on its semantics. As an example, in the claims.csv file, the AirportCode implies AirportName, as each code should be unique for a given airport.

The file ground_truth.csv is a comma separated file containing approximate functional dependencies. table describes the relation we refer to, lhs and rhs reference two columns of those relations where semantically we found that lhs implies rhs.

The file excluded_candidates.csv and included_candidates.csv list all column combinations that were excluded or included in the manual annotation, respectively. We excluded a candidate if there was no tuple where both attributes had a value or if the g3_prime value was too small.

Dataset References

• adult.csv: Dua, D. and Graff, C. (2019). UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science.
• claims.csv: TSA Claims Data 2002 to 2006, published by the U.S. Department of Homeland Security.
• dblp10k.csv: Frequency-aware Similarity Measures. Lange, Dustin; Naumann, Felix (2011). 243–248. Made available as DBLP Dataset 2.
• hospital.csv: Hospital dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. Made available as part the dataset collection to that paper.
• t_biocase_... files: t_bioc_... files used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. Made available as part the dataset collection to that paper.
• tax.csv: Tax dataset used in Johann Birnick, Thomas Bläsius, Tobias Friedrich, Felix Naumann, Thorsten Papenbrock, and Martin Schirneck. 2020. Hitting set enumeration with partial information for unique column combination discovery. Proc. VLDB Endow. 13, 12 (August 2020), 2270–2283. https://doi.org/10.14778/3407790.3407824. Made available as part the dataset collection to that paper.

This database contains benchmark results for simulation of plasma population kinetics and emission spectra. The data were contributed by the participants of the 4th Non-LTE Code Comparison Workshop who have unrestricted access to the database. The only limitation for other users is in hidden labeling of the output results. Guest users can proceed to the database entry page without entering userid and password.

The European Business Performance database describes the performance of the largest enterprises in the twentieth century. It covers eight countries that together consistently account for above 80 per cent of western European GDP: Great Britain, Germany, France, Belgium, Italy, Spain, Sweden, and Finland. Data have been collected for five benchmark years, namely on the eve of WWI (1913), before the Great Depression (1927), at the extremes of the golden age (1954 and 1972), and in 2000.The database is comprised of two distinct datasets. The Small Sample (625 firms) includes the largest enterprises in each country across all industries (economy-wide). To avoid over-representation of certain countries and sectors, countries contribute a number of firms that is roughly proportionate to the size of the economy: 30 firms from Great Britain, 25 from Germany, 20 from France, 15 from Italy, 10 from Belgium, Spain, and Sweden, and 5 from Finland. By the same token, a cap has been set on the number of financial firms entering the sample, so that they range between up to 6 for Britain and 1 for Finland.The second dataset, or Large Sample (1,167 firms), is made up of the largest firms per industry. Here industries are so selected as to take into account long-term technological developments and the rise of entirely new products and services. Firms have been individually classified using the two-digit ISIC Rev. 3.1 codes, then grouped under a manageable number of industries. To some extent and broadly speaking, the two samples have a rather distinct focus: the Small Sample is biased in favour of sheer bigness, whereas the Large Sample emphasizes industries.As far as size and performance indicators are concerned, total assets has been picked as the main size measure in the first three benchmarks, turnover in 1972 and 2000 (financial intermediaries, though, are ranked by total assets throughout the database). Performance is gauged by means of two financial ratios, namely return on equity and shareholders’ return, i.e. the percentage year-on-year change in share price based on year-end values. In order to smooth out volatility, at each benchmark performance figures have been averaged over three consecutive years (for instance, performance in 1913 reflects average performance in 1911, 1912, and 1913).All figures were collected in national currency and converted to US dollars at current year-average exchange rates.

The following dataset includes "Active Benchmarks," which are provided to facilitate the identification of City-managed standard benchmarks. Standard benchmarks are for public and private use in establishing a point in space. Note: The benchmarks are referenced to the Chicago City Datum = 0.00, (CCD = 579.88 feet above mean tide New York). The City of Chicago Department of Water Management’s (DWM) Topographic Benchmark is the source of the benchmark information contained in this online database. The information contained in the index card system was compiled by scanning the original cards, then transcribing some of this information to prepare a table and map. Over time, the DWM will contract services to field verify the data and update the index card system and this online database.This dataset was last updated September 2011. Coordinates are estimated. To view map, go to https://data.cityofchicago.org/Buildings/Elevation-Benchmarks-Map/kmt9-pg57 or for PDF map, go to http://cityofchicago.org/content/dam/city/depts/water/supp_info/Benchmarks/BMMap.pdf. Please read the Terms of Use: http://www.cityofchicago.org/city/en/narr/foia/data_disclaimer.html.

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.

Usefulness of metadata in the automatic version of ACMANTv5 was tested.
A benchmark database has been developed, which consists of 41 datasets
of 20,500 networks of 170,000 synthetic monthly temperature time series
and the relating metadata dates. The research was supported by the
Catalan Meteorological Service. The research results will be published
in the open access MDPI journal Atmosphere.

See more in the "Readme.txt" file of the dataset.

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

In the last two decades, alignment analyses have become an important technique in quantitative historical linguistics and dialectology. Phonetic alignment plays a crucial role in the identification of regular sound correspondences and deeper genealogical relations between and within languages and language families. Surprisingly, up to today, there are no easily accessible benchmark data sets for phonetic alignment analyses. Here we present a publicly available database of manually edited phonetic alignments which can serve as a platform for testing and improving the performance of automatic alignment algorithms. The database consists of a great variety of alignments drawn from a large number of different sources. The data is arranged in a such way that typical problems encountered in phonetic alignment analyses (metathesis, diversity of phonetic sequences) are represented and can be directly tested.

NADA (Not-A-Database) is an easy-to-use geometric shape data generator that allows users to define non-uniform multivariate parameter distributions to test novel methodologies. The full open-source package is provided at GIT:NA_DAtabase. See Technical Report for details on how to use the provided package.

This database includes 3 repositories:

• NADA_Dis: Is the model able to correctly characterize/Disentangle a complex latent space?
  The repository contains 3x100,000 synthetic black and white images to test the ability of the models to correctly define a proper latent space (e.g., autoencoders) and disentangle it. The first 100,000 images contain 4 shapes and uniform parameter space distributions, while the other images have a more complex underlying distribution (truncated Gaussian and correlated marginal variables).
  
  
• NADA_OOD: Does the model identify Out-Of-Distribution images?
  The repository contains 100,000 training images (4 different shapes with 3 possible colors located in the upper left corner of the canvas) and 6x100,000 increasingly different sets of images (changing the color class balance, reducing the radius of the shape, moving the shape to the lower left corner) providing increasingly challenging out-of-distribution images.
  This can help to test not only the capability of a model, but also methods that produce reliability estimates and should correctly classify OOD elements as "unreliable" as they are far from the original distributions.
  
  
• NADA_AlEp: Does the model distinguish between different types (Aleatoric/Epistemic) of uncertainties?
  The repository contains 5x100,000 images with different type of noise/uncertainties:
  
  • NADA_AlEp_0_Clean: Dataset clean of noise to use as a possible training set.
  • NADA_AlEp_1_White_Noise: Epistemic white noise dataset. Each image is perturbed with an amount of white noise randomly sampled from 0% to 90%.
  • NADA_AlEp_2_Deformation: Dataset with Epistemic deformation noise. Each image is deformed by a randomly amount uniformly sampled between 0% and 90%. 0% corresponds to the original image, while 100% is a full deformation to the circumscribing circle.
  • NADA_AlEp_3_Label: Dataset with label noise. Formally, 20% of Triangles of a given color are missclassified as a Square with a random color (among Blue, Orange, and Brown) and viceversa (Squares to Triangles). Label noise introduces \textit{Aleatoric Uncertainty} because it is inherent in the data and cannot be reduced.
  • NADA_AlEp_4_Combined: Combined dataset with all previous sources of uncertainty.

Each image can be used for classification (shape/color) or regression (radius/area) tasks.

All datasets can be modified and adapted to the user's research question using the included open source data generator.