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.

This Data Access Agreement (DAA) is freely available to use and is intended for use where data is accessed within a Trusted Research Environment (TRE) for the purposes of research and development for the public good. The DAA has been developed by the TRE Legal Toolkit Action Force of the Pan UK Data Governance Steering Group. The Pan UK Data Governance Steering Group is a working Group of the UK Health Data Research Alliance representing data custodians and policymakers across the four nations. The Steering Group is focused on simplifying and streamlining data access governance processes. The DAA terms and conditions should not be modified. The annexes are customisable to allow for differences between TREs. New with Version 6.0 we are providing a Personal Data and a Non-Personal Data template The latter intended for use only where the data accessed in the TRE and the Data Output are both not considered Personal Data or Confidential Patient Information (CPI) from the perspective of the Approved User and their Approved User Organisation who are accessing the data on behalf of the Sponsor Organisation. If research will involve access to Data that is Personal Data or CPI, or the creation of Data Outputs that are likely to become Personal Data or CPI in the hands of the intended recipients, the Personal Data version must be used. We wish to encourage widespread adoption of this template and it is freely available to use. If you do plan to adopt this template or would like to discuss any queries please get in touch with: Rachel Brophy, Head of Information and Research Governance, HDR UK (rachel.brophy@hdruk.ac.uk) cc: informationgovernance@hdruk.ac.uk Please see version control document for details of changes.

One of the well-established methods used to ease data sharing between organisations and even teams within organisations is to use standards for data structure, metadata and interfaces. Standards are a form of agreement, as are MoUs, charters, deeds, licences, rules of the road and even the definitions for words. Man y of these other sorts of agreements are also important for data sharing communities too. In this paper we look to improve the efficiency of dealing with different forms of agreement within a data sharing scenario by presenting a prototype agreements ontology which models agreements themselves as ¿things¿ and the relationships between them and between them and data and them and agents. Having an agreements ontology allows us to start automating tasks that require knowledge of them. This may take the form of data repositories that can make intelligent choices about how to deliver or with old data without human intervention. We position this ontology as a 'middle' ontology, that is one which specializes well-known, abstract, upper ontologies and is able to be used fairly widely but is expected to be used in particular contexts in conjunction with detailed, domain-specific, lower ontologies. We have relied on existing agent, data manipulation, and metadata ontologies where possible and as such we specialise the ORG and FOAF ontologies, the PROV ontology and DCAT and ODRS ontologies for those areas respectively. This paper and ontology supports work that we report elsewhere at SciDataCon2016, namely The Role of Social Architecture in Information Infrastructure (Box & Lemon) and Describing and Automating Requirements within Licenses and their Resolutions (Car & Stenson).

Service Agreement on Storage and Dissemination of Research Data A service agreement for the storage and dissemination of research data is required to use SND's repository (SND CARE) for research data. The agreement details the legal prerequisites for the use of SND CARE and the various commitments and responsibilities of SND and the research principal, respectively. Members of the SND Network are advised to sign a general service agreement for the use of SND CARE. For individual researchers who do not belong to a research principal that is a member of the SND Network, or if the research principal has not signed a general service agreement with SND, an agreement is signed digitally in SND’s system for describing and sharing data (DORIS) per submitted dataset. This document is the agreement template. The terms of the contract may vary slightly for individual parties. The agreement is translated into English, but for reading only. The Swedish version is the official agreement and the one which is to be signed. Uppdragsavtal avseende lagring och förmedling För att använda SND:s repositorium (SND CARE) för forskningsdata tecknas ett Uppdragsavtal avseende lagring och förmedling av forskningsdata med SND. Avtalet redogör bland annat för de rättsliga förutsättningarna för användandet av SND CARE, samt innehåller de olika åtaganden och ansvar som åligger SND respektive forskningshuvudmannen. Medlemmar i SND-nätverket rekommenderas att teckna ett generellt uppdragsavtal för användning av SND CARE. För enskilda forskare som inte tillhör en forskningshuvudman som är medlem i SND:s nätverk, eller om forskningshuvudmannen inte har tecknat ett generellt uppdragsavtal med SND, signeras ett avtal per inlämnat dataset digitalt i DORIS (SND:s system för att beskriva och dela data). Det som delas här är den avtalsmall som SND använder. Avtalsvillkoren kan variera något med enskilda parter. Avtalet finns översatt till engelska, men enbart för läsning. Det är den svenska versionen av avtalet som är den formella och som signeras.

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.

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

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.

Empower Your Business With Professional Data Licensing Services

Discover a 360-Degree View of Worldwide Solution Buyers and Their Needs Leverage over 70 insights that will help you make better decisions to manage your sales pipeline, target key accounts with customized messaging, and focus your sales and marketing efforts:

Here are some of the types of Insights, our data licensing services can provide are:

Technology Insights: Discover companies’ technology preferences, including their tech stack for essential investments such as CRM systems, marketing and sales automation, email security and hosting, data analytics, and cloud security and providers.

Departmental Roles and Openings: Access real-time data on the number of roles and job openings across various departments, including IT, Development, Security, Marketing, Sales, and Customer Success. This information helps you gauge the company’s growth trajectory and possible needs.

Funding Insights: Keep updated of the latest funding, dates, types, and lead investors, providing you with a clear understanding of a company’s potential for growth investments.

Mobile Application Insights: Find out if the company has a mobile app or web app, enabling you to tailor your pitch effectively.

Website traffic and advertising spend metrics: Customers can leverage website traffic and advertising data to gain insights into competitor performance, allowing them to refine their marketing strategies and optimize ad spending.

Access unlimited data and improve conversation by 3X

• Leverage the data for your Account-Based Marketing (ABM) strategy

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Talent sourcing/Staffing companies- Leverage our data to identify and engage top talent, streamlining your recruitment process and finding the best candidates faster.

Research companies/Investors- Insights into the financial investments received by a company, including funding rounds, amounts, and investor details.

Technology companies: Leverage our Technographic data to reveal the technology stack and tools used by companies, helping tailor marketing and sales efforts.

Data Source:

The Database, sourced through multiple sources and validated using proprietary methods on an ongoing basis, is highly customizable. It contains parameters such as employee size, job title, domain, industry, Technography, Ad spends, Funding data, and more, which can be tailored to create segments that perfectly align with your targeting needs. That is exactly why our Database is perfect for licensing!

FAQs

1. Can licensed data be resold or redistributed? Answer: No, The customer shall not, directly or indirectly, sell, distribute, license, or otherwise make available the licensed data to any third party that intends to resell, sublicense, or redistribute the data. The Customer must take reasonable steps to ensure that any recipient of the licensed data is using it for internal purposes only and not for resale or redistribution. Any breach of this provision shall be considered a material breach of this Order Form and may result in the immediate termination of the Customer's rights under this agreement, as well as any applicable remedies available under law.

2. What is the duration of the data license and usage terms? Answer: The data license is valid for 12 months (1 year) for unlimited usage. Customers also have the option to license the data for multiple years. At the end of the first year, Customers can renew the license to maintain continued access.

3. What happens if the customer misuses the data? Answer: The data can be used without limits for a period of one year or multiple years (depending on the contract tenure); however, Thomson Data actively monitors its usage. If any unusual activity is detected, Thomson Data reserves the right to terminate the account.

4. How frequently is the data updated? Answer: The data is updated on a quarterly basis and fresh records added on a monthly basis

5. What is the accuracy rate of the data? Answer: Customers can expect 90% accuracy for all data points, with email accuracy ranging between 85% and 90%. Cell phone data accuracy is around 80%.

6. What types of information are included in the data? Answer: Thomson Data provides over 70+ data points, including contact details (name, job title, LinkedIn profile, cell number, email address, education, certifications, work experience, etc.), company information, department/team sizes, SIC and NAICS codes, industry classification, technographic detai...

Simple reference for agency and category teams to search and export all tier rated contract information, including the enhanced fields used by the data team to determine the master and derivative contract information required to determine the appropriate tier level.

This is the final contract with the vendor. It shows what they will provide to Louisville Metro.This is the transparency page for the Russell Public WiFi project that is part of the Smart Russell initiative from the Office of Civic Innovation & Technology. The purpose of this page is to provide documentation related to the project so the public understand and verify the intentions of the project and the outcomes.Every month, the project manager will put up the usage numbers for the network and the access logs for the data. This is to show who is accessing the data and to share the data that is accessed to provide transparency to the public about what information is being collected by the network.Data Dictionary:Location = Text description of the location, will not help with mapping itx = latitudey = longitudeNotes = context and information about the access pointsHomepage URL

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.

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.

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

The Healthcare Cost and Utilization Project (HCUP) National Inpatient Sample (NIS) is the largest publicly available all-payer inpatient care database in the United States. The NIS is designed to produce U.S. regional and national estimates of inpatient utilization, access, cost, quality, and outcomes. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Developed through a Federal-State-Industry partnership sponsored by the Agency for Healthcare Research and Quality (AHRQ), HCUP data inform decision making at the national, State, and community levels. Starting with the 2012 data year, the NIS is a sample of discharges from all hospitals participating in HCUP, covering more than 97 percent of the U.S. population. For prior years, the NIS was a sample of hospitals. The NIS allows for weighted national estimates to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. The NIS's large sample size enables analyses of rare conditions, such as congenital anomalies; uncommon treatments, such as organ transplantation; and special patient populations, such as the uninsured. NIS data are available since 1988, allowing analysis of trends over time. The NIS inpatient data include clinical and resource use information typically available from discharge abstracts with safeguards to protect the privacy of individual patients, physicians, and hospitals (as required by data sources). Data elements include but are not limited to: diagnoses, procedures, discharge status, patient demographics (e.g., sex, age), total charges, length of stay, and expected payment source, including but not limited to Medicare, Medicaid, private insurance, self-pay, or those billed as ‘no charge’. The NIS excludes data elements that could directly or indirectly identify individuals. Restricted access data files are available with a data use agreement and brief online security training.

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.

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 geospatial market is estimated to reach a value of USD 105.06 billion by 2033, growing at a CAGR of 9.1% during the forecast period of 2025-2033. This growth is attributed to the rising demand for geospatial data and services across various end-user industries, such as real estate and construction, automotive, utility and communication, government, defense and intelligence, and natural resources. Key drivers of the market include the increasing adoption of location-based services, the growing use of geospatial data for decision-making, and the rapid advancements in geospatial technologies. The market is segmented by type (surface analytics, network analytics, geovisualization, and others), technology (remote sensing, GPS, GIS, and others), and end-user (real estate and construction, automotive, utility and communication, government, defense and intelligence, natural resources, and others). North America is the largest regional market, followed by Europe, Asia Pacific, and the Middle East and Africa. Key companies in the geospatial market include Hexagon AB, Trimble Inc., Maxar Technologies, MDA Corporation, Fugro, Cyient, Esri, Bentley Systems, Incorporated, NV5 Global Inc., General Electric Company, Accenture, and RMSI. Recent developments include: In February 2023, U.S. Army has extended its contract with Maxar Technologies to provide 3D geospatial data used to create immersive digital environments. Maxar was awarded Phase 3b of the U.S. Army’s One World Terrain (OWT) contract originally awarded in 2019 to Vricon, a company Maxar acquired in 2020. Vricon uses data from Maxar’s imaging satellites to make 3D mapping products. In September 2023, Trimble and Kyivstar Partner to Provide GNSS Correction Services for Agriculture, Construction and Geospatial Applications in Ukraine. It will install a new Continuously Operating Reference Station (CORS) network to provide Global Navigation Satellite System (GNSS) correction services across the country. Available to users as an annual subscription service, the new network will be built using Trimble's hardware and software positioning technology, which provides customers with easy and reliable high-accuracy real time or post-processed GNSS corrections data for agriculture, construction, geospatial, Internet of Things (IoT) and other commercial operations. . Key drivers for this market are: Growing demand for accurate and actionable geospatial data

Advancements in cloud computing and AI technologies

Increasing adoption of location-based services and IoT devices

Need for sustainable resource management and environmental monitoring

Government initiatives and investments in geospatial infrastructure. Potential restraints include: Data privacy and security concerns

Lack of skilled workforce and technical expertise

High cost of implementing and maintaining geospatial solutions

Interoperability issues between different geospatial platforms

Limited awareness of the benefits of geospatial technologies. Notable trends are: The integration of AI and IOT fuels Integration of blockchain technology for data security and transparency

Development of open-source geospatial platforms and applications

Use of AI and machine learning for predictive analytics and automation

Focus on interconnected and interoperable geospatial ecosystems

Growing adoption of geospatial solutions in emerging marketsmarket expansion is expected to drive market growth..

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/