Whether you're building an eCommerce dashboard, researching market trends, or prototyping beauty intelligence tools — this dataset is a perfect place to start.

Interested in Live Tracking or API?

This dataset represents just a snapshot of what we track in real time at https://beautyfeeds.io/" target="_new" rel="noopener" data-start="2004" data-end="2041">BeautyFeeds:

• Monitor price & stock changes daily or weekly

• Track products from major retailers like Sephora, Ulta, Nykaa, Amazon, and more

• Access via export or live API

• Filter by brand, country, or category

• Assign custom URLs for targeted scraping

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The Inorganic Crystal Structure Database (ICSD) is produced cooperatively by the Fachinformationszentrum Karlsruhe (FIZ) and the National Institute of Standards and Technology (NIST). Components and devices used in a broad spectrum of technology sectors such as health care, communications, energy and electronics are manufactured from crystalline materials; the development of advanced crystalline materials requires accurate crystal-structure data. SRD 84 ICSD provides critically evaluated, comprehensive crystal-structure data and search software that enable phase identification by their characteristic diffraction patterns using X-rays, neutrons and electrons. SRD 84 ICSD contains full crystallographic and atomic-position information for more than 180,000 non-organic materials, including inorganics, ceramics, minerals, pure elements, metals and intermetallics, published in literature from 1913 through the present. ICSD is updated twice a year, with each update comprising about 2,000 to 10,000 new or re-evaluated entries. Data items include bibliographic information, compound designation such as chemical name, chemical formula, mineral name; and crystallographic parameters such as unit cell, space group, element symbol with numbering, oxidation state, multiplicity for Wyckoff position, x,y,z coordinates, site occupation, thermal parameters and reliability index R, among others. A free demonstration CD which includes a comprehensive user interface, FindIt, for a demo database is available upon request or can be downloaded from the website https://www.nist.gov/srd/nist-standard-reference-database-84.

A structure database of natural products in SDF format was created from the LOTUS database version 9 .

This database is intended to facilitate the dereplication of natural products.

The LOTUS database was described in this publication (free download).

File 220916_frozen_metadata.csv was downloaded from the LOTUS database version 9 and the SMILES chains of the compounds were collected.

The SMILES chains were translated to 2D chemical structures using python scripts relying on the RDKit library.

Each compound was associated to predicted 13C NMR chemical shifts by means of an already reported procedure (free download).

Each compound was also supplemented with metadata from file 220916_frozen_metadata.csv .

Archive file acd_lotusv9.sdf.zip contains acd_lotusv9.sdf with 218,478 compound descriptions inside.

Archive file acd_lotusv9.NMRUDB.zip is a compressed version of acd_lotusv9.NMRUDB, itself created by importation of file acd_lotusv9.sdf in an ACD/Labs database file (new with version 0.0.4).

The description of the first compound was copied in file firstmolv9.sdf and is provided for a quick inspection of the database content.

The title line in firstmolv9.sdf is Q43656_2, meaning that more data about this compound may be found by searching in Wikidata for Q43656 and that the initial data was given by line 2 in file 220916_frozen_metadata.csv .

Files acd_lotusv9.sdf acd_lotusv9.NMRUDB contain biological taxonomy data from file 220916_frozen_metadata.csv that were not exploited in acd_lotusv7. Sub-files dealing with a particular taxon can be easily produced now.

Chemical shift calculations for 13C nuclei using the HOSE code approach are available here for the compounds in acd_lotusv7.

this graph was created in R and Canva :

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F1a47e2e6e4836b86b065441359d5c9f0%2Fgraph1.gif?generation=1742159161939732&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2F87de025c5703cb69483764c4fc9c58ab%2Fgraph2.gif?generation=1742159169346925&alt=media" alt=""> https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F16731800%2Fddf5001438c97c8c030333261685849b%2Fgraph3.png?generation=1742159174793142&alt=media" alt="">

The dataset offers a comprehensive view of grocery inventory, covering 990 products across multiple categories such as Grains & Pulses, Beverages, Fruits & Vegetables, and more. It includes crucial details about each product, such as its unique identifier (Product_ID), name, category, and supplier information, including Supplier_ID and Supplier_Name. This dataset is particularly valuable for businesses aiming to optimize inventory management, sales tracking, and supply chain efficiency.

Key inventory-related fields include Stock_Quantity, which indicates the current stock level, and Reorder_Level, which determines when a product should be reordered. The Reorder_Quantity specifies how much stock to order when inventory falls below the reorder threshold. Additionally, Unit_Price provides insight into pricing, helping businesses analyze cost trends and profitability.

To manage product flow, the dataset includes dates such as Date_Received, which tracks when the product was added to the warehouse, and Last_Order_Date, marking the most recent procurement. For perishable goods, the Expiration_Date column is critical, allowing businesses to minimize waste by monitoring shelf life. The Warehouse_Location specifies where each product is stored, facilitating efficient inventory handling.

Sales and performance metrics are also included. The Sales_Volume column records the total number of units sold, providing insights into consumer demand. Inventory_Turnover_Rate helps businesses assess how quickly a product sells and is replenished, ensuring better stock management. The dataset also tracks the Status of each product, indicating whether it is Active, Discontinued, or Backordered.

The dataset serves multiple purposes in inventory management, sales performance evaluation, supplier analysis, and product lifecycle tracking. Businesses can leverage this data to refine reorder strategies, ensuring optimal stock levels and avoiding stockouts or excessive inventory. Sales analysis can help identify high-demand products and slow-moving items, enabling better decision-making in pricing and promotions. Evaluating suppliers based on their performance, pricing, and delivery efficiency helps streamline procurement and improve overall supply chain operations.

Furthermore, the dataset can support predictive analytics by employing machine learning techniques to estimate reorder quantities, forecast demand, and optimize stock replenishment. Inventory turnover insights can aid in maintaining a balanced supply, preventing unnecessary overstocking or shortages. By tracking trends in sales, businesses can refine their marketing and distribution strategies, ensuring sustained profitability.

This dataset is designed for educational and demonstration purposes, offering fictional data under the Creative Commons Attribution 4.0 International License. Users are free to analyze, modify, and apply the data while providing proper attribution. Additionally, certain products are marked as discontinued or backordered, reflecting real-world inventory dynamics. Businesses dealing with perishable goods should closely monitor expiration and last order dates to avoid losses due to spoilage.

Overall, this dataset provides a versatile resource for those interested in inventory management, sales analysis, and supply chain optimization. By leveraging the structured data, businesses can make data-driven decisions to enhance operational efficiency and maximize profitability.

The MIMIC Chest X-ray JPG (MIMIC-CXR-JPG) Database v2.0.0 is a large publicly available dataset of chest radiographs in JPG format with structured labels derived from free-text radiology reports. The MIMIC-CXR-JPG dataset is wholly derived from MIMIC-CXR, providing JPG format files derived from the DICOM images and structured labels derived from the free-text reports. The aim of MIMIC-CXR-JPG is to provide a convenient processed version of MIMIC-CXR, as well as to provide a standard reference for data splits and image labels. The dataset contains 377,110 JPG format images and structured labels derived from the 227,827 free-text radiology reports associated with these images. The dataset is de-identified to satisfy the US Health Insurance Portability and Accountability Act of 1996 (HIPAA) Safe Harbor requirements. Protected health information (PHI) has been removed. The dataset is intended to support a wide body of research in medicine including image understanding, natural language processing, and decision support.

Various population statistics, including structured demographics data.

Our travel datasets provide extensive, structured data covering various aspects of the global travel and hospitality industry. These datasets are ideal for businesses, analysts, and developers looking to gain insights into hotel pricing, short-term rentals, restaurant listings, and travel trends. Whether you're optimizing pricing strategies, analyzing market trends, or enhancing travel-related applications, our datasets offer the depth and accuracy you need.

Key Travel Datasets Available:

  Hotel & Rental Listings: Access detailed data on hotel properties, short-term rentals, and vacation stays from platforms like 
    Airbnb, Booking.com, and other OTAs. This includes property details, pricing, availability, guest reviews, and amenities.

  Real-Time & Historical Pricing Data: Track hotel room pricing, rental occupancy rates, and pricing trends 
    to optimize revenue management and competitive analysis.

  Restaurant Listings & Reviews: Explore restaurant data from Tripadvisor, OpenTable, Zomato, Deliveroo, and Talabat, 
    including restaurant details, customer ratings, menus, and delivery availability.

  Market & Trend Analysis: Use structured datasets to analyze travel demand, seasonal trends, and consumer preferences 
    across different regions.

  Geo-Targeted Data: Get location-specific insights with city, state, and country-level segmentation, 
    allowing for precise market research and localized business strategies.



Use Cases for Travel Datasets:

  Dynamic Pricing & Revenue Optimization: Adjust pricing strategies based on real-time market trends and competitor analysis.
  Market Research & Competitive Intelligence: Identify emerging travel trends, monitor competitor performance, and assess market demand.
  Travel & Hospitality App Development: Enhance travel platforms with accurate, up-to-date data on hotels, restaurants, and rental properties.
  Investment & Financial Analysis: Evaluate travel industry performance for investment decisions and economic forecasting.



  Our travel datasets are available in multiple formats (JSON, CSV, Excel) and can be delivered via 
  API, cloud storage (AWS, Google Cloud, Azure), or direct download. 
  Stay ahead in the travel industry with high-quality, structured data that powers smarter decisions.

USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Ranger Station, White House, and City/Town Hall. Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. Included is a feature class of preliminary building polygons provided by FEMA, USA Structures. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain structures data in either Esri File Geodatabase or Shapefile formats. For additional information on the structures data model, go to https://www.usgs.gov/ngp-standards-and-specifications/national-map-structures-content.

This comprehensive IKEA USA products dataset contains detailed information about thousands of authentic IKEA furniture items, home decor, and household products available in the United States market. The dataset provides complete product specifications, pricing, availability, and detailed descriptions for ecommerce analysis, price comparison, and furniture retail research.

Key Features:

• Complete IKEA USA product catalog with real pricing data
• Detailed product descriptions and specifications
• Product URLs, article numbers, and availability status
• Furniture categories including office chairs, storage solutions, outdoor furniture
• Home decor items like candle holders, planters, and textiles
• Kitchen cabinets, wardrobes, and organizational systems
• Material specifications and sustainability information
• Product dimensions, weights, and packaging details

Get Free Sample: Download your free sample dataset now to explore the data quality and structure before purchasing the complete IKEA USA products database. The free sample includes representative product entries with all key fields populated.

Applications: Perfect for furniture market analysis, home improvement research, interior design planning, competitive pricing analysis, and retail intelligence. This dataset enables businesses to understand IKEA pricing strategies, product positioning, and market trends in the home furnishing industry.

Product Categories Included: Office furniture, bedroom furniture, storage solutions, outdoor dining sets, kitchen systems, home organization products, decorative accessories, plant containers, and sustainable furniture options. All products include comprehensive details for business intelligence and market research applications.

Download Free Sample
The data center market in southeast asia structure is fragmented and the vendors are deploying various organic and inorganic strategies to compete in the market. Some of the key vendors operating in the global data center market in southeast asia are:

Alphabet Inc.Amazon.com Inc.Colt Technology Services Group Ltd.Digital Realty Trust Inc.Equinix Inc.Global Switch Holdings Ltd.International Business Machines Corp.Microsoft Corp.NTT Communications Corp.Singapore Telecommunications Ltd.

The data center market in southeast asia research report offers comprehensive vendor information and analysis that help in getting a clear picture of the competitive landscape of the market.

THIS RESOURCE IS NO LONGER IN SERVICE. Documented on February 28,2023. Database of secondary structure assignments (and much more) for all protein entries in the Protein Data Bank (PDB) and the program that calculates DSSP entries from PDB entries. DSSP is distributed on a basis of trust and instructions are available on the site. * Precompiled executables are also available for Linux and Windows. (The Windows .exe file was compiled under Linux using Mingw32, has never seen a Windows environment and should thus be virus-free. Download the source if you want to be 100% sure.) Under Windows the DSSP output does not make it to the console, so redirect it to a file instead: dsspcmbi source.pdb destination.dssp > messages.txt * Several changes have been made to the DSSP program to solve problems with recent PDB files. These are documented in the source code. * FTP access to the DSSP files resides at the CMBI: ftp.cmbi.kun.nl/pub/molbio/data/dssp or ftp://ftp.ebi.ac.uk/pub/databases/dssp/. If you have problems downloading the DSSP files, it is likely that your FTP program is not able to handle tens of thousands of files in one directory. In this case, install a proper FTP program, for example NCFTP. However, it is recommended that you download DSSP files with the rsync command.

This repository contains databases of protein domains for use with Foldclass and Merizo-search. We provide databases for all 365 million domains in TED, as well as all classified domains in CATH 4.3.Foldclass and Merizo-search use two formats for databases. The default format uses a PyTorch tensor and a pickled list of Python tuples to store the data. This format is used for the CATH database, which is small enough to fit in memory. For larger-than-memory datasets, such as TED, we use a binary format that is searched using the Faiss library.The CATH database requires approximately 1.4 GB of disk space, whereas the TED database requires about 885 GB. Please ensure you have enough free storage space before downloading. For best search performance with the TED database, the database should be stored on the fastest storage hardware available to you.IMPORTANT:We recommend going in to each folder and downloading the files; if you attempt to download each folder in one go, it will download a zip file which will need to be decompressed. This is particularly an issue if downloading the TED database, as you will need to have roughly twice the storage space needed as compared to downloading the individual files. Our GitHub repository (see Related Materials below) contains a convenience script to download each database; we recommend using that.

Reproduction material. See 'readme' for further details. Use 'tree view' to view the folder structure of the data and download the full dataset to retain the original folder structure. Data wrangling was done in Stata, data analysis was done in R.

Download Free Sample
The machine learning market structure is fragmented. The market vendors are increasingly focusing on quality, pricing, and innovation to strengthen their position in the machine learning market. The machine learning market research report offers comprehensive vendor information and analysis that help in getting a clear picture of the competitive landscape of the market.

Some of the key vendors operating in the global machine learning market are:

Alibaba Group Holding Ltd.Alphabet Inc.Amazon.com Inc.Cisco Systems Inc.Hewlett Packard Enterprise Development LPInternational Business Machines Corp.Microsoft Corp.Salesforce.com Inc.SAP SESAS Institute Inc.

The AlphaFold Protein Structure Database is a collection of protein structure predictions made using the machine learning model AlphaFold. AlphaFold was developed by DeepMind , and this database was created in partnership with EMBL-EBI . For information on how to interpret, download and query the data, as well as on which proteins are included / excluded, and change log, please see our main dataset guide and FAQs . To interactively view individual entries or to download proteomes / Swiss-Prot please visit https://alphafold.ebi.ac.uk/ . The current release aims to cover most of the over 200M sequences in UniProt (a commonly used reference set of annotated proteins). The files provided for each entry include the structure plus two model confidence metrics (pLDDT and PAE). The files can be found in the Google Cloud Storage bucket gs://public-datasets-deepmind-alphafold-v4 with metadata in the BigQuery table bigquery-public-data.deepmind_alphafold.metadata . If you use this data, please cite: Jumper, J et al. Highly accurate protein structure prediction with AlphaFold. Nature (2021) Varadi, M et al. AlphaFold Protein Structure Database: massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Research (2021) This public dataset is hosted in Google Cloud Storage and is available free to use. Use this quick start guide to quickly learn how to access public datasets on Google Cloud Storage.

The vibration datasets were acquired from three rectangular beams (Aluminum, Brass, and Copper beams), with different properties and geometric specifications, and with different boundary conditions, as shown in the attached figure. The data collection was performed via the integrated smartphone accelerometer with the following specifications:

• The acquisition was performed using a smartphone with a sampling rate of 100Hz.
• The reference name of the smartphone accelerometer is BOSCH BMC150.
• The three beams have been subjected to free vibration by applying a load and removing it.
• The acquisition includes the vibration data in the vertical direction (Z-axis) according to the smartphone coordinate system.
• The data files contain 3 columns of data, where the first one is according to X-direction, the second is for Y-direction, and the third is for Z-direction (Z-axis).

The objective of this study is to extract the dynamic characteristics of the beams and compare them with the theoretical values in order to present an alternative instrument for mechanical and civil engineering students to practice their theoretical courses in structural vibration.

NB: - More details are included in the reference below. - The reported datasets are entirely linked to the scientific article: "Dynamic characteristics of beams under free vibration using the smartphone - laboratory experiments for education", So, it is recommended to read it.

Eurostat data contains many indicators (short-term, structural, theme-specific and others) on the EU-28 and the Eurozone, the Member States and their partners. The database of Eurostat contains always the latest version of the datasets meaning that there is no versioning on the data. Datasets are updated twice a day, at 11:00 and at 23:00, in case new data is available or because of structural change.

It is possible to access the datasets through SDMX Web Services, as well as through Json and Unicode Web Services.

SDMX Web Services are a programmatic access to Eurostat data, with the possibility to:

• get a complete list of publicly available datasets;
• detail the complete structure definition of a given dataset;
• download a subset of a given dataset or a full dataset.

SDMX Web Services:

• provide access to datasets listed under database by themes, and predefined tables listed under tables by themes;
• provide data in SDMX 2.0 and 2.1 formats;
• support both Representation State Transfer (REST) and Simple Object Access Protocol (SOAP) protocols;
• return responses in English language only;
• are free of charge.

The JSON & UNICODE Web Services are a programmatic access to Eurostat data, with the possibility to download a subset of a given dataset. This operation allows customizing requests for data. You can filter on dimensions to retrieve specific data subsets.

The JSON & UNICODE Web Services:

• provide data in JSON-stat and UNICODE formats;
• support only Representation State Transfer (REST) protocol;
• deliver responses in English, French and German language;
• are free of charge.

Download Free Sample
The higher education market structure is fragmented and the vendors are deploying various organic and inorganic strategies to compete in the market. Some of the key vendors operating in the global higher education market are:

Adobe Inc.Apple Inc.Blackboard Inc.Dell Inc.D2L Corp.Discovery Inc.Ellucian Co. LPInstructure Inc.Pearson plcSamsung Electronics Co. Ltd

The higher education market research report offers comprehensive vendor information and analysis that help in getting a clear picture of the competitive landscape of the market.

This CBIS-DDSM (Curated Breast Imaging Subset of DDSM) is an updated and standardized version of the Digital Database for Screening Mammography (DDSM). The DDSM is a database of 2,620 scanned film mammography studies. It contains normal, benign, and malignant cases with verified pathology information. The scale of the database along with ground truth validation makes the DDSM a useful tool in the development and testing of decision support systems. The CBIS-DDSM collection includes a subset of the DDSM data selected and curated by a trained mammographer. The images have been decompressed and converted to DICOM format. Updated ROI segmentation and bounding boxes, and pathologic diagnosis for training data are also included. A manuscript describing how to use this dataset in detail is available at https://www.nature.com/articles/sdata2017177.

Published research results from work in developing decision support systems in mammography are difficult to replicate due to the lack of a standard evaluation data set; most computer-aided diagnosis (CADx) and detection (CADe) algorithms for breast cancer in mammography are evaluated on private data sets or on unspecified subsets of public databases. Few well-curated public datasets have been provided for the mammography community. These include the DDSM, the Mammographic Imaging Analysis Society (MIAS) database, and the Image Retrieval in Medical Applications (IRMA) project. Although these public data sets are useful, they are limited in terms of data set size and accessibility.

For example, most researchers using the DDSM do not leverage all its images for a variety of historical reasons. When the database was released in 1997, computational resources to process hundreds or thousands of images were not widely available. Additionally, the DDSM images are saved in non-standard compression files that require the use of decompression code that has not been updated or maintained for modern computers. Finally, the ROI annotations for the abnormalities in the DDSM were provided to indicate a general position of lesions, but not a precise segmentation for them. Therefore, many researchers must implement segmentation algorithms for accurate feature extraction. This causes an inability to directly compare the performance of methods or to replicate prior results. The CBIS-DDSM collection addresses that challenge by publicly releasing an curated and standardized version of the DDSM for evaluation of future CADx and CADe systems (sometimes referred to generally as CAD) research in mammography.

Please note that the image data for this collection is structured such that each participant has multiple patient IDs. For example, participant 00038 has 10 separate patient IDs which provide information about the scans within the IDs (e.g. Calc-Test_P_00038_LEFT_CC, Calc-Test_P_00038_RIGHT_CC_1). This makes it appear as though there are 6,671 patients according to the DICOM metadata, but there are only 1,566 actual participants in the cohort.

For scientific and other inquiries about this dataset, please contact TCIA's Helpdesk.

This layer is part of Hamilton City Council's Stormwater Dataset.If you wish to download and consume this entire dataset - click on the link for the file format(s) of your choosing:CAD (DWG) Please note that the links above may change at any time. For best practice, please refer to this page for the correct links.   If any of the links are above are not functioning, please let us know at gis@hcc.govt.nz. This Stormwater dataset contains the following layers: Stormwater Abandoned Main (A stormwater main that is still in the ground, but is now disused and no longer forms part of the active network) Stormwater Abandoned Manhole (An opening in a pipe for the purpose of allowing operators or equipment access to the inside of the pipe that is still in the ground but is now disused and no longer forms part of the active network) Stormwater Asbuilts (Plans showing the location and alignment of basic stormwater infrastructure as it was actually constructed on site, as provided by the contractor or their representatives.  Data has not yet been fully incorporated into the Council GIS or asset management system) Stormwater Attenuation and Treatment Device (A device used to provide temporary storage and the controlled release of storm water volumes. Located upstream of the receiving environment, devices such as wetlands and ponds may also incorporate a storm water treatment function) Stormwater Catchpit (A device that collects stormwater run-off from the road and transports it along the network) Stormwater Catchpit Lead (A pipe that transports stormwater run-off from catchpits and connects into the stormwater network) Stormwater Channel (An open drain, natural watercourse (such as a stream) or lined channel that collects stormwater run-off from the environment or network) Stormwater Inlet (A structure where stormwater enters either a pipe, pond, culvert or channel) Stormwater Main (A pipe that transports stormwater to a natural watercourse or body of water) Stormwater Manhole (An opening in a pipe for the purpose of allowing operators or equipment access to the inside of the pipe) Stormwater Node (A junction point in a pipe. It can be a structure) Stormwater Outlet (A structure at the end of a pipe or channel that controls the flow of stormwater to a natural watercourse or body of water)Stormwater Service Line (A gravity flow pipeline connecting a building’s direct runoff collection system to a stormwater pipe or a kerb (in the case of kerb and channel connections)) Stormwater Soakage Trench (A subsurface structure into which runoff is conveyed for disposal by infiltration) Stormwater Subsoil Drain (A perforated drain used to collect ground water and transport it to a land drainage or stormwater drainage system)  Hamilton City Council 3 Waters data is derived from the Council’s GIS (ArcGIS) dataset. The GIS dataset is synchronised with asset data contained in the Council’s Asset Management (IPS) database. A subset of the GIS dataset has been made available for download. This GIS dataset is currently updated weekly which in turn dynamically updates to the WLASS open data site. Any questions pertaining to this data should be directed to the City Waters Asset Information Team at CityWatersAssetInfo@hcc.govt.nz Hamilton City Council does not make any representation or give any warranty as to the accuracy or exhaustiveness of the data released for public download. Levels, locations and dimensions of works depicted in the data may not be accurate due to circumstances not notified to Council. A physical check should be made on all levels, locations and dimensions before starting design or works. Hamilton City Council shall not be liable for any loss, damage, cost or expense (whether direct or indirect) arising from reliance upon or use of any data provided, or Council's failure to provide this data. While you are free to crop, export and re-purpose the data, we ask that you attribute the Hamilton City Council and clearly state that your work is a derivative and not the authoritative data source. Please include the following statement when distributing any work derived from this data: ‘This work is derived entirely or in part from Hamilton City Council data; the provided information may be updated at any time, and may at times be out of date, inaccurate, and/or incomplete.