This document presents the standard starting point language to use when drafting a formal data sharing agreement between a City entity and either another City entity or an outside party when two parties seek to share non-public data with one another. The document outlines the following major concerns:Parties to the agreementPurpose of the data sharing effort Period of the agreementDescription of the data to be sharedTiming and frequency of updates to the shared dataPoint(s) of contactCustodial responsibilitiesMethod of data transferPublication ReviewOther City terms and conditions This version 1.1 makes minor corrections of language originally formalized by the City's Data Governance Committee in June of 2020. Note that a data sharing agreement is not final or authorized without appropriate signatures from all parties represented by the agreement.

We include a description of the data sets in the meta-data as well as sample code and results from a simulated data set. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: The R code is available on line here: https://github.com/warrenjl/SpGPCW. Format: Abstract The data used in the application section of the manuscript consist of geocoded birth records from the North Carolina State Center for Health Statistics, 2005-2008. In the simulation study section of the manuscript, we simulate synthetic data that closely match some of the key features of the birth certificate data while maintaining confidentiality of any actual pregnant women. Availability Due to the highly sensitive and identifying information contained in the birth certificate data (including latitude/longitude and address of residence at delivery), we are unable to make the data from the application section publicly available. However, we will make one of the simulated datasets available for any reader interested in applying the method to realistic simulated birth records data. This will also allow the user to become familiar with the required inputs of the model, how the data should be structured, and what type of output is obtained. While we cannot provide the application data here, access to the North Carolina birth records can be requested through the North Carolina State Center for Health Statistics and requires an appropriate data use agreement. Description Permissions: These are simulated data without any identifying information or informative birth-level covariates. We also standardize the pollution exposures on each week by subtracting off the median exposure amount on a given week and dividing by the interquartile range (IQR) (as in the actual application to the true NC birth records data). The dataset that we provide includes weekly average pregnancy exposures that have already been standardized in this way while the medians and IQRs are not given. This further protects identifiability of the spatial locations used in the analysis. File format: R workspace file. Metadata (including data dictionary) • y: Vector of binary responses (1: preterm birth, 0: control) • x: Matrix of covariates; one row for each simulated individual • z: Matrix of standardized pollution exposures • n: Number of simulated individuals • m: Number of exposure time periods (e.g., weeks of pregnancy) • p: Number of columns in the covariate design matrix • alpha_true: Vector of “true” critical window locations/magnitudes (i.e., the ground truth that we want to estimate). This dataset is associated with the following publication: Warren, J., W. Kong, T. Luben, and H. Chang. Critical Window Variable Selection: Estimating the Impact of Air Pollution on Very Preterm Birth. Biostatistics. Oxford University Press, OXFORD, UK, 1-30, (2019).

This dataset includes the results of the pilot activity that Public Services and Procurement Canada undertook as part of Canada’s 2018-2020 National Action Plan on Open Government. The purpose is to demonstrate the usage and implementation of the Open Contracting Data Standard (OCDS). OCDS is an international data standard that is used to standardize how contracting data and documents can be published in an accessible, structured, and repeatable way. OCDS uses a standard language for contracting data that can be understood by all users. ###What procurement data is included in the OCDS Pilot? Procurement data included as part of this pilot is a cross-section of at least 250 contract records for a variety of contracts, including major projects. ###Methodology and lessons learned The Lessons Learned Report documents the methodology used and the lessons learned during the process of compiling the pilot data.

The Business Management System (VVS) used by UAB Šiauliai vandenys accumulates data on the service accounts provided from 2017. The dataset collects customer contract data. Collection data coverage in time – from 1991. The dataset consists of: contract identifier, customer legal entity name or anonymised name of natural person, legal or natural person number, type of person (natural/legal), date of conclusion, start and end of contract, whether it is a water or sewage service contract. One entry corresponds to one contract. Although, according to the available data, VMS data has been compiled since 2017, the data used to compile the collection includes dates from 1753 to 2122. The first number of years due to frequency has been considered to be the equivalent of non-existent data, so dates for that year have been changed to “null” values. The remaining dates are limited from 1991 to today's date. The dataset consists of single use data and is therefore not updated. Data provider – UAB Šiauliai vandenys. Contact the atverimas@stat.gov.lt for technical questions or possible errors.

Contracts and modifications awarded by the City of Chicago since 1993. This data is currently maintained in the City’s Financial Management and Purchasing System (FMPS), which is used throughout the City for contract management and payment. Legacy System Records: Purchase Order/Contract Numbers that begin with alpha characters identify records imported from legacy systems. Records with a null value in the Contract Type field were imported from legacy systems. "Comptroller-Other" Contract Type: Some records where the Contract Type is "COMPTROLLER-OTHER" are ordinance-based agreements and may have start dates earlier than 1993. Depends Upon Requirements Contracts: If the contract Award Amount is $0, the contract is not cancelled, and the contract is a blanket contract, then the contract award total Depends Upon Requirements. A Depends Upon Requirements contract is an indefinite quantities contract in which the City places orders as needed and the vendor is not guaranteed any particular contract award amount.

Blanket vs. Standard Contracts: Only blanket contracts (contracts for repeated purchases) have FMPS end dates. Standard contracts (for example, construction contracts) terminate upon completion and acceptance of all deliverables. These dates are tracked outside of FMPS.

Negative Modifications: Some contracts are modified to delete scope and money from a contract. These reductions are indicated by negative numbers in the Award Amount field of this dataset.

Data Owner: Procurement Services. Time Period: 1993 to present. Frequency: Data is updated daily.

The San Francisco Controller's Office maintains a database of contracts and payments made to Suppliers (vendors, contractors and grantees). The Supplier Contracts shown here include all fully executed, expenditure-only contracts, including City contracts for commodities, professional and general services, and construction from fiscal year 2018 forward, based upon the contracting departments' inputs. This data set includes both departmental and citywide contracts.

This data is presented on the Supplier Contracts report hosted at http://openbook.sfgov.org, and is also available in CSV format, which lists summary data by contract. We have removed sensitive contracts and information from this data in order to balance disclosure requirements and protected/confidential data. For example, we have removed contracts related to certain legal proceedings that are exempt from disclosure. New data is added on a weekly basis.

Revenue contracts, including concessions and rental income contracts, are not included in this data set; they are tracked in departmental subsystems and currently are not available in SF Open Book. For more information about Revenue Contracts, please contact the respective City Department that awards the contract, e.g. for Airport Concession Contracts, contact the San Francisco Airport Commission which maintains these records.

City Department spending may occur three ways: 1) from Contract to Purchase Order (i.e. a PO with an accounting Encumbrance) to Payment (i.e. an accounting Voucher), AKA On-Contract spending, 2) direct-to-Purchase Order (no contract, only a PO agreement and accounting Encumbrance) to Payment, and 3) Direct Voucher Payment (for limited one-time, non-recurring payments, e.g. a membership or a subscription). Therefore, it is helpful to use both the Supplier Contracts and Supplier Payments reports on SFOpenBook, as well as on DataSF.

The data included in the Supplier Contracts dataset reflects supplier expenditure contracts that were awarded by City Departments after 7/1/2017, the date the City & County of San Francisco implemented the PeopleSoft Financial & Procurement System, as well as the remaining not-to-exceed contract authority for all supplier expenditure contracts that were still active along with their associated purchase order (PO) available, unexpended balances as of 7/1/2017. For partially used contract authority, the Contract Award Amount only includes the portion that was unutilized and available as of 7/1/2017, unless the Contract Award Amount was updated by the department after July 1, 2017. Contracts that were expired before 7/1/2017 as well as contracts that had no remaining contracting authority from which to issue new purchase orders were not converted or loaded into PeopleSoft. For information requests regarding the latter as well as for the portion of contract of authority used prior to 7/1/2017 for otherwise unexpired contract balances that were converted and still active as of 7/1/2017, please contact the City department(s) that issued the contract.

The Department of Licensing (DOL) shares data under the strict terms of a data sharing agreement. People and organizations agree to undergo regular data security and permissible use audits. This dataset is a record of the audits that DOL conducts each year.

Information on the current Across Government ICT Contracts, suppliers and how these contracts are to be used with the South Australian Government.

According to Cognitive Market Research, the global Contract Intelligence market size is USD 815.2 million in 2023 and will expand at a compound annual growth rate (CAGR) of 23.80% from 2023 to 2030.

North America held the major market of more than 40% of the global revenue with a market size of USD 326.08 million in 2023 and will grow at a compound annual growth rate (CAGR) of 22.0% from 2023 to 2030
Europe market of more than 30% of the global revenue with a market size of USD 244.56 million in 2023 and will grow at a compound annual growth rate (CAGR) of 22.3% from 2023 to 2030.
Asia Pacific held the fastest growing market of more than 23% of the global revenue with a market size of USD 187.50 million in 2023 and will grow at a compound annual growth rate (CAGR) of 25.8% from 2023 to 2030.
Latin America market of more than 5% of the global revenue with a market size of USD 40.76 million in 2023 and will grow at a compound annual growth rate (CAGR) of 23.2% from 2023 to 2030
The Middle East and Africa market of more than 2.00% of the global revenue with a market size of USD 16.30 million in 2023 and will grow at a compound annual growth rate (CAGR) of 23.5% from 2023 to 2030
The demand for Contract Intelligence is rising due to efficiency gains through automation risk,k mitigation, and compliance enhancement.
Demand for software remains higher in the Contract Intelligence market.
The cloud category held the highest Contract Intelligence market revenue share in 2023.

Digital Transformation Imperative to Provide Viable Market Output

The key driver fuelling the Contract Intelligence market is the overarching imperative for organizations to undergo comprehensive digital transformation. As businesses recognize the limitations of manual and paper-based contract management processes, the demand for Contract Intelligence solutions rises. The need for enhanced efficiency, accessibility, and automation in handling contracts is driving companies to adopt AI-powered tools that can streamline workflows, improve collaboration, and provide actionable insights. The relentless pursuit of digital transformation is propelling the Contract Intelligence market forward as businesses seek innovative solutions to manage their expanding volumes of contracts in a more agile and intelligent manner.

In June 2022, Icertis was selected by the Ooredoo Group as a provider of contract intelligence. Ooredoo intends to use the Contract Intelligence CLM platform to produce extensive sets of documents in a organized and interconnected manner to guarantee that all data is correctly documented throughout the enterprise.

(Source:www.icertis.com/company/news/ooredoo-selects-icertis-contract-intelligence-as-part-of-enterprise-digital-transformation/)

Growing Complexity of Contracts to Propel Market Growth

Another critical driver shaping the Contract Intelligence market is the increasing complexity of contracts in the modern business landscape. Contracts have evolved to encompass intricate legal language, diverse terms, and a multitude of variables, making manual review and management a daunting task. Contract Intelligence addresses this challenge by leveraging artificial intelligence and machine learning to swiftly analyse and interpret complex contracts. This capability not only enhances accuracy and reduces the risk of errors but also allows organizations to extract valuable insights from contracts, contributing to informed decision-making. The escalating intricacies of contracts across various industries are driving the adoption of Contract Intelligence solutions to effectively navigate this complexity and ensure compliance.

May 2022, Icertis has introduced an AI studio to provide AI-driven contract intelligence to a wide audience. The AI studio will enable customers to swiftly analyze extensive documents and various aspects of business agreements to make prompt decisions.

(Source:www.icertis.com/company/news/icertis-releases-ai-studio-to-democratize-use-of-ai-powered-contract-intelligence/)

Market Restraints of the Contract Intelligence

Data Privacy and Security Concerns to Restrict Market Growth

A prominent restraint in the Contract Intelligence market revolves around heightened concerns regarding data privacy and security. As organizations increasingly rely on AI-driven tools to manage and analyse their contracts, the sensitivity of the information within these contracts raises apprehens...

The Government Support Services (GSS) office within Office of Management of Budget (OMB) administers statewide central contracts for use by state agencies, school districts, and municipalities. This dataset provides line item spend data as reported by vendors awarded central contracts through monthly submission of vendor usage reports.

Note: Reporting of new COVID-19 Case Surveillance data will be discontinued July 1, 2024, to align with the process of removing SARS-CoV-2 infections (COVID-19 cases) from the list of nationally notifiable diseases. Although these data will continue to be publicly available, the dataset will no longer be updated.

Authorizations to collect certain public health data expired at the end of the U.S. public health emergency declaration on May 11, 2023. The following jurisdictions discontinued COVID-19 case notifications to CDC: Iowa (11/8/21), Kansas (5/12/23), Kentucky (1/1/24), Louisiana (10/31/23), New Hampshire (5/23/23), and Oklahoma (5/2/23). Please note that these jurisdictions will not routinely send new case data after the dates indicated. As of 7/13/23, case notifications from Oregon will only include pediatric cases resulting in death.

This case surveillance public use dataset has 12 elements for all COVID-19 cases shared with CDC and includes demographics, any exposure history, disease severity indicators and outcomes, presence of any underlying medical conditions and risk behaviors, and no geographic data.

CDC has three COVID-19 case surveillance datasets:

• COVID-19 Case Surveillance Public Use Data with Geography: Public use, patient-level dataset with clinical data (including symptoms), demographics, and county and state of residence. (19 data elements)
• COVID-19 Case Surveillance Public Use Data: Public use, patient-level dataset with clinical and symptom data and demographics, with no geographic data. (12 data elements)
• COVID-19 Case Surveillance Restricted Access Detailed Data: Restricted access, patient-level dataset with clinical and symptom data, demographics, and state and county of residence. Access requires a registration process and a data use agreement. (33 data elements)

• Data elements can be found on the COVID-19 case report form located at www.cdc.gov/coronavirus/2019-ncov/downloads/pui-form.pdf.
• Data are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers.
• Some data cells are suppressed to protect individual privacy.
• The datasets will include all cases with the earliest date available in each record (date received by CDC or date related to illness/specimen collection) at least 14 days prior to the creation of the current datasets. This 14-day lag allows case reporting to be stabilized and ensures that time-dependent outcome data are accurately captured.
• Datasets are updated monthly.
• Datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access and include protections designed to protect individual privacy.
• For more information about data collection and reporting, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/about-us-cases-deaths.html.
• For more information about the COVID-19 case surveillance data, please see https://www.cdc.gov/coronavirus/2019-ncov/covid-data/faq-surveillance.html
  

Overview

The COVID-19 case surveillance database includes individual-level data reported to U.S. states and aut

Listing of all purchase orders and contracts issued to procure goods and/or services within City-Parish.

In the City-Parish, a PO/Contract is made up of two components: a header and one or many detail items that comprise the overarching PO/Contract. The header contains information that pertains to the entire PO/Contract. This includes, but is not limited to, the total amount of the PO/Contract, the department requesting the purchase and the vendor providing the goods or services. The detail item(s) contain information that is specific to the individual item ordered or service procured through the PO/Contract. The item/service description, item/service quantity and the cost of the item is located within the PO/Contract details. There may be one or many detail items on an individual PO/Contract. For example, a Purchase Order for a computer equipment may include three items: the computer, the monitor and the base software package.

Both header information and detail item information are included in this dataset in order to provide a comprehensive view of the PO/Contract data. The Record Type field indicates whether the record is a header record (H) or detail item record (D). In the computer purchase example from above, the system would display 4 records – one header record and 3 detail item records.

It should be noted header information will be duplicated on all detail items. No detail item information will be displayed on the header record.

***In October of 2017, the City-Parish switched to a new system used to track PO/Contracts. This data contains all PO/Contracts entered in or after October 2017. For prior year data, please see the Legacy Purchase Order dataset https://data.brla.gov/Government/Legacy-Purchase-Orders/54bn-2sqf

Contracts and modifications awarded by the City of Chicago since 1993. This data is currently maintained in the City’s Financial Management and Purchasing System (FMPS), which is used throughout the City for contract management and payment. Legacy System Records: Purchase Order/Contract Numbers that begin with alpha characters identify records imported from legacy systems. Records with a null value in the Contract Type field were imported from legacy systems. "Comptroller-Other" Contract Type: Some records where the Contract Type is "COMPTROLLER-OTHER" are ordinance-based agreements and may have start dates earlier than 1993. Depends Upon Requirements Contracts: If the contract Award Amount is $0, the contract is not cancelled, and the contract is a blanket contract, then the contract award total Depends Upon Requirements. A Depends Upon Requirements contract is an indefinite quantities contract in which the City places orders as needed and the vendor is not guaranteed any particular contract award amount.

Blanket vs. Standard Contracts: Only blanket contracts (contracts for repeated purchases) have FMPS end dates. Standard contracts (for example, construction contracts) terminate upon completion and acceptance of all deliverables. These dates are tracked outside of FMPS.

Negative Modifications: Some contracts are modified to delete scope and money from a contract. These reductions are indicated by negative numbers in the Award Amount field of this dataset.

Data Owner: Procurement Services. Time Period: 1993 to present. Frequency: Data is updated daily.

These data include PDF files of 1496 health insurance contracts from the year 2019 in the United States. They come from 40 corporations [435 self-insured plans using third party administrators (TPAs)] and health insurance contracts from the individual, small group, and large group markets sold in the states of CA (852 plans) and MI (209 plans). The Excel spreadsheet is our coding sheet for all of these contracts analyzing transgender health care coverage options.

We drew these contracts from AXIACI from Leverage Global Consulting, a proprietary database that contains insurance plan offerings and coverage from private and public insurance market segments. Our use of the proprietary database for the purposes of public policy research and analysis in health insurance is governed by a data use agreement between Leverage and Professor Anna Kirkland.

We use the term “contract” to mean the Summary Plan Documents (SPD), which is the roughly 100-200 page document that an individual gets from their health insurance company describing coverage and exclusions. These are the documents contained here. Other documents include the Member Handbook, Certificate of Coverage, Summary of Benefits and Coverage, Subscribers Contract, medical policies, and drug formulary, all of which are distinct and differently regulated documents.

A subset of Tenders Electronic Daily (TED) data covering public procurement for the European Union and beyond from 2006-01-01 to 2023-12-31 in comma separated value (CSV) format. This data includes the most important fields from the contract notice and contract award notice standard forms, such as who bought what from whom, for how much, and which procedure and award criteria were used.

Generally, the data consists of tenders above the procurement thresholds. However, publishing below threshold tenders in TED is considered good practice, and thus a non-negligible number of below threshold tenders is present as well.

Please see the documentation below for important information on the data and its usage, including a version history of the export.

The European Commission is interested in the results of research on public procurement coming from the re-use of this data. Thus, we will be grateful to receive links to any papers, reports, or applications at GROW-G4@ec.europa.eu.

TED with broader coverage is also available in XML format at https://data.europa.eu/euodp/en/data/dataset/ted-1.

eForms

On 14 November 2022, the format of notices published in TED changed: the Publications Office displays both the current standard forms and eForms and makes them available for reuse. If you reuse TED data, your systems must be ready to process both types of notices. To help adapt your systems, you can find resources, models and schemas in the eForms Software Development Kit on GitHub (https://github.com/OP-TED/eForms-SDK/https://github.com/OP-TED/eForms-SDK/). Documentation is available on the Ted Developers Documentation site (https://docs.ted.europa.eu/), including eForms FAQs (https://docs.ted.europa.eu/home/FAQ/eforms.html).

Analysis of ‘Supplier Contracts’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/534c1caf-36ba-4e3d-a53a-c4a1f872cfe8 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

The San Francisco Controller's Office maintains a database of contracts and payments made to Suppliers (vendors, contractors and grantees). The Supplier Contracts shown here include all fully executed, expenditure-only contracts, including City contracts for commodities, professional and general services, and construction from fiscal year 2018 forward, based upon the contracting departments' inputs. This data set includes both departmental and citywide contracts.

This data is presented on the Supplier Contracts report hosted at http://openbook.sfgov.org, and is also available in CSV format, which lists summary data by contract. We have removed sensitive contracts and information from this data in order to balance disclosure requirements and protected/confidential data. For example, we have removed contracts related to certain legal proceedings that are exempt from disclosure. New data is added on a weekly basis.

Revenue contracts, including concessions and rental income contracts, are not included in this data set; they are tracked in departmental subsystems and currently are not available in SF Open Book. For more information about Revenue Contracts, please contact the respective City Department that awards the contract, e.g. for Airport Concession Contracts, contact the San Francisco Airport Commission which maintains these records.

City Department spending may occur three ways: 1) from Contract to Purchase Order (i.e. a PO with an accounting Encumbrance) to Payment (i.e. an accounting Voucher), AKA On-Contract spending, 2) direct-to-Purchase Order (no contract, only a PO agreement and accounting Encumbrance) to Payment, and 3) Direct Voucher Payment (for limited one-time, non-recurring payments, e.g. a membership or a subscription). Therefore, it is helpful to use both the Supplier Contracts and Supplier Payments reports on SFOpenBook, as well as on DataSF.

--- Original source retains full ownership of the source dataset ---

Healthcare Contract Research Outsourcing Market size was valued at USD 43.22 Billion in 2024 and is projected to reach USD 75.66 Billion by 2032, growing at a CAGR of 7.25% during the forecast period 2026-2032.

Global Healthcare Contract Research Outsourcing Market Drivers

The market drivers for the Healthcare Contract Research Outsourcing Market can be influenced by various factors. These may include:

Increasing Research and Clinical Trial Involvement: There has been an increase in research and development activity in the biotechnology and pharmaceutical sectors, which has raised the need for clinical trial outsourcing. Healthcare CROs are essential to the success of these trials and the expansion of the market as a whole.

Time savings and cost effectiveness: Drug development can proceed more quickly and with lower operating expenses for pharmaceutical and biotech businesses that outsource their clinical research operations to CROs. CROs frequently have specialized infrastructure and knowledge, which boosts productivity and shortens turnaround times.

Global Clinical Trials Industry: The increasing number of clinical trials being done in different parts of the world has led to the demand for CROs with local knowledge who can manage a variety of trial populations and negotiate regulatory environments.

The intricacy of the drug development process: With developments in personalized medicine, genetics, and medical research, the process of developing new drugs has grown increasingly intricate. To take use of CROs' specialized resources and skills, pharmaceutical corporations outsource different parts of clinical trials to them.

Strategic Emphasis on Fundamental Skills: Many biotech and pharmaceutical businesses would rather outsource their clinical research operations to specialized CROs and concentrate on their core skills, such marketing and drug discovery. They are able to distribute resources more skillfully as a result.

The State Contract and Procurement Registration System (SCPRS) was established in 2003, as a centralized database of information on State contracts and purchases over $5000. eSCPRS represents the data captured in the State's eProcurement (eP) system, Bidsync, as of March 16, 2009. The data provided is an extract from that system for fiscal years 2012-2013, 2013-2014, and 2014-2015

Data Limitations:
Some purchase orders have multiple UNSPSC numbers, however only first was used to identify the purchase order. Multiple UNSPSC numbers were included to provide additional data for a DGS special event however this affects the formatting of the file. The source system Bidsync is being deprecated and these issues will be resolved in the future as state systems transition to Fi$cal.

Data Collection Methodology:

The data collection process starts with a data file from eSCPRS that is scrubbed and standardized prior to being uploaded into a SQL Server database. There are four primary tables. The Supplier, Department and United Nations Standard Products and Services Code (UNSPSC) tables are reference tables. The Supplier and Department tables are updated and mapped to the appropriate numbering schema and naming conventions. The UNSPSC table is used to categorize line item information and requires no further manipulation. The Purchase Order table contains raw data that requires conversion to the correct data format and mapping to the corresponding data fields. A stacking method is applied to the table to eliminate blanks where needed. Extraneous characters are removed from fields. The four tables are joined together and queries are executed to update the final Purchase Order Dataset table. Once the scrubbing and standardization process is complete the data is then uploaded into the SQL Server database.

Secondary/Related Resources:

• State Contract Manual (SCM) vol. 2 http://www.dgs.ca.gov/pd/Resources/publications/SCM2.aspx
• State Contract Manual (SCM) vol. 3 http://www.dgs.ca.gov/pd/Resources/publications/SCM3.aspx
• Buying Green http://www.dgs.ca.gov/buyinggreen/Home.aspx
• United Nations Standard Products and Services Code, http://www.unspsc.org/

This dataset consolidates all the proactive publication of contract reports submitted by federal government entities. Note: The information contained in these Contracts reports have been derived from data provided by the authorized federal reporting entities. As the data is unaudited, no warranty or other legal assurance as to the accuracy or completeness of this information is stated or can be implied. Any person making use of this information for any purpose does so at his or her own risk, and the Government of Canada shall have no liability. For information regarding the specifics of the contract data provided in these Contract reports, please contact the authorized federal reporting entity identified in the contract data.

The Contract Notice Export is an export file containing contract notice data for all Australian Government agencies who report procurement on AusTender published over a one week period. new Contract Notice Export file is available for download from AusTender every Sunday. Contract Notice Export files remain available on AusTender for a period of eighteen (18) months.

You can find the historical contract data dating back to 1999 at "http://data.gov.au/dataset/historical-australian-government-contract-%0Adata/">http://data.gov.au/dataset/historical-australian-government-contract- data/

Please note that the data contained within each CN Export file is correct at the time of extraction. Data may change over time as agencies undertake data quality assurance processes or as contract information is varied. Please refer to the tenders website below for the latest of each week.

**Note: **As part of the implementation of the new Commonwealth Procurement Rules (CPRs), Procurement Method descriptions for Contract and Standing Offer Notices reported on AusTender changed, effective 1 July 2012. Open, Select and Direct are now Open Tender, Prequalified Tender, and Limited Tender, respectively. For Contract Notice Export files extracted prior to 1 July 2012, Procurement Method descriptions are unchanged.

Source of information at "http://www.tenders.gov.au/?event=public.reports.listCNWeeklyE%0Axport">http://www.tenders.gov.au/?event=public.reports.lis tCNWeeklyExport

UPDATE: Now below is also the current list of goods and services category codes used by Australian Government agencies to code their open approaches to market and contracts awarded for publication on AusTender. The codeset is a subset of the United Nations Standard Product and Services Code (UNSPSC) and is reviewed and updated periodically. Code numbers starting with the prefixes 43 a Information Technology, Broadcasting and Telecommunications and 81 a Engineering and Research and Technology Based Services apply in the main to Information and Communications Technology related contracts, primarily ICT goods and services respectively.

Please note: we will make an API available to this dataset in the near future.