Finesse Benchmark Database

  Overview

finesse-benchmark-database is a data generation factory for atomic probes in the Finesse benchmark. It generates probes_atomic.jsonl files from Wikimedia Wikipedia datasets, leveraging Hugging Face's datasets library, tokenizers from transformers, and optional PyTorch support. This tool is designed to create high-quality, language-specific probe datasets for benchmarking fine-grained understanding in NLP tasks.… See the full description on the dataset page: https://huggingface.co/datasets/enzoescipy/finesse-benchmark-database.

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.

Dataset Description

This dataset provides a benchmark for automatic data product creation. The task is framed as follows: given a natural language data product request and a corpus of text and tables, the objective is to identify the relevant tables and text documents that should be included in the resulting data product which would useful to the given data product request. The benchmark brings together three variants: HybridQA, TAT-QA, and ConvFinQA, each consisting of:

A corpus… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/data-product-benchmark.

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

LakeBench: Benchmarks for Data Discovery over Data Lakes

Version 3 adds the wiki-join-search benchmark used in the "join search" experiments in our paper.

The data in the labels files (i.e., labels.json files or files under the labels folder) are shared under Attribution-ShareAlike 4.0 International (CC BY-SA 4.0) license: https://creativecommons.org/licenses/by-sa/4.0/

The data in the tables folder comes from different sources under various open licenses, as detailed in the README.txt file in each folder. All the datasets included in the benchmark have been verified to have a public license that allows distribution, derivatives, and commercial use.

THIS DATA IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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

Point geometry with attributes displaying geodetic control stations (benchmarks) in East Baton Rouge Parish, Louisiana.

Results of the IGUANA Benchmark in 2015/16 for the truncated DBpedia dataset. The dataset is 10% of the initial 100% dataset.

About

"Blender is the free and open source 3D creation suite. It supports the entirety of the 3D pipeline—modeling, rigging, animation, simulation, rendering, compositing and motion tracking, even video editing and game creation." -- from Blender's about page.

Blender is community-driven. Its popularity as one of the best freeware in open source community has driving more and more people to use it. But, as like the other 3D creation suite softwares, Blender is quite demanding on hardware requirements. Luckily Blender can be used on almost all kind of common devices sold in the market. It gives the users freedom to choose whichever devices they have. Unfortunately, not all devices are equal. Some are great, others are underperformed.

Blender gives a way for users to checking how their devices might performed when using their product by benchmarking data. The Blender open data provide the user with benchmarking data which the user might find by querying.

This dataset was created based on the queries in Blender open data. It provides concise benchmarking data by aggregating the score of all number of benchmarks for each devices.

The Goals

I don't have much goals when I made this dataset, but I want to create a database that easy to use to help Blender users to know whether their device is proper to use Blender, especially for version 3.1.0, or they might want to find other devices that suitable.

Acknowledgement

All data was taken from Blender Open Data. Cover image from Unsplash.com by @bertsz

The Building Performance Database (BPD) is the largest publicly-available source of measured energy performance data for buildings in the United States. It contains information about the building's energy use, location, and physical and operational characteristics. The BPD can be used by building owners, operators, architects and engineers to compare a building's energy performance against customized peer groups, identify energy performance opportunities, and set energy performance. It can also be used by energy performance program implementers to analyze energy performance features and trends in the building stock. The BPD compiles data from various data sources, converts it into a standard format, cleanses and quality checks the data, and provides users with access to the data in a way that maintains anonymity for data providers.

The BPD consists of the database itself, a graphical user interface allowing exploration of the data, and an application programming interface allowing the development of third-party applications using the data.

This dataset contains data from a motion analysis experiment using a markerless and a marker-based system. Its purpose is to benchmark whole-body markerless motion analysis methods. A GitHub repository (https://github.com/lbmc-lyon/Benchmarking_markerless) is associated with this dataset, where one may find and upload kinematics obtained from video data. The data consisted of recordings with both systems of five different tasks performed by two participants. The tasks cover various activities and challenges for both systems (gait, sit-to-stand-to-sit transfers, manual box handling, challenging motions with high feet, and couple dancing). The dataset includes: "marker" data (3D trajectories of 48 skin reflective markers placed on specific anatomical landmarks) provided as .c3d files; RGB video from 9 calibrated and synchronized cameras provided as .avi files; Utility files such as camera calibration files.

Performance comparison on the benchmark noisy database.

Dummy Dataset for AutoTrain Benchmark

This dataset contains dummy data that's needed to create AutoTrain projects for benchmarks like RAFT. See here for more details.

Benchmark test databases for IQA.

This data set contains data related to the paper 'TrafPy: Benchmarking Data Centre Network Systems'. The data have been split into 3 files to avoid needing to download all data sets if only some are needed:1) plotData: The data plotted in the paper for each of the benchmarks averaged across 5 runs.2) trafficData: The flow-centric traffic requests used in each of the simulations.3) simulationData: Each individual benchmark run. Contains full access to the simulation history, metrics, and so on. When unzipped, this file is ~2.5 TB in size.

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.

net traffic

Global Database Performance Monitoring Services comes with the extensive industry analysis of development components, patterns, flows and sizes. The report also calculates present and past market values to forecast potential market management through the forecast period between 2024 - 2032. The report may be the best of what is a geographic area which expands the competitive landscape and industry perspective of the market.

LLMSQL Benchmark

This benchmark is designed to evaluate text-to-SQL models. For usage of this benchmark see https://github.com/LLMSQL/llmsql-benchmark. Arxiv Article: https://arxiv.org/abs/2510.02350

  Files

tables.jsonl — Database table metadata questions.jsonl — All available questions train_questions.jsonl, val_questions.jsonl, test_questions.jsonl — Data splits for finetuning, see https://github.com/LLMSQL/llmsql-benchmark sqlite_tables.db — sqlite db with tables from… See the full description on the dataset page: https://huggingface.co/datasets/llmsql-bench/llmsql-benchmark.

Dataset

This dataset was created by Konrad Banachewicz

Released under Database: Open Database, Contents: © Original Authors

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