Types of Data Sample Clauses
The "Types of Data" clause defines and categorizes the various forms of data that are relevant to the agreement or relationship between the parties. It typically specifies distinctions such as personal data, confidential information, aggregated data, or usage data, and may outline how each type is treated or protected under the contract. By clearly identifying and differentiating data types, this clause ensures that both parties understand their rights and obligations regarding data handling, thereby reducing ambiguity and helping to ensure compliance with applicable laws and contractual requirements.
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Types of Data. “TxDOT Data” means TxDOT information, data, records, and information to which a Contractor-Related Entity has access, has possession, or is otherwise provided to the a Contractor-Related Entity by TxDOT, whether or not intended under or for the purposes of the agreement, including, without limitation, data generated or collected under this agreement, intellectual property created as a work for hire under this agreement, and Personal Identifying Information (as defined below). TxDOT Data is classified into the four categories that control applicability of security standards: Public, Sensitive, Confidential, and Regulated. See Section 4 for Definitions. Any data that a Contractor-Related Entity accesses and downloads from a TxDOT system, for use, manipulation, storage, or management is considered Confidential Data unless otherwise specified in writing by TxDOT.
Types of Data. 1.1 Raw: data submitted by Participants (e.g., DITL);
Types of Data. Institution Data may include, but information, contact details, professional activities and affiliations, professional qualifications, education and training, financial information on the educational grant provided by Medtronic to Institution, and bank account information. In case Institution provides unsolicited additional information, including personal preferences, Medtronic will process such data in accordance with this data protection clause. Medtronic may identify certain characteristics, or traits on the basis of which Medtronic may create or compile professional, compromiso entre las partes respecto al objeto del mismo y sustituye a todos los acuerdos o compromisos anteriores celebrados entre las partes en relación con su objeto.
Types of Data. For the purposes of this set of documents there are essentially three classes of data as defined by the Act itself listed below:
2.1 Anonymised and Aggregated Data
Types of Data. Almost 3.000 data attributes can be exchanged via My Product Manager. These attributes are determined by leading retailers and suppliers/manufacturers in the Benelux do-it-yourself industry. The attributes can roughly be divided into several groups, based on their relevance and purpose: - Common attributes - eCommerce attributes - Attributes for dangerous goods and environmental legislation You will find more information about these group of attributes in the next sections.
1.2.1 Common attributes
1.2.2 eCommerce attributes
Types of Data. Data will be gathered to establish baseline conditions, evaluate impacts of Covered Activities on Covered Species, and develop conservation strategies and measures for Covered Species. Data needed to accomplish these tasks may include, but will not necessarily be limited to: species life histories, species occurrence, population abundance and distribution, population trends, population genetics, habitat locations and conditions, hydrologic regime, hydrodynamics, temperature, flow patterns, water quality, barrier and hazard types and locations, habitat connectivity, ecological threats and stressors.
Types of Data. Institution Data may include, but are not limited to, individuals’ basic identity information, contact details, professional activities and affiliations, professional qualifications, education and training, financial information on the educational grant provided by Medtronic to Institution, and bank account information. In case Institution provides unsolicited additional information, including personal preferences, Medtronic will process such data in accordance with this data protection clause. Medtronic may identify certain characteristics, or traits on the basis of which Medtronic may create or compile professional, compromiso entre las partes respecto al objeto del mismo y sustituye a todos los acuerdos o compromisos anteriores celebrados entre las partes en relación con su objeto.
Types of Data. The SFY 2022 encounter data for the Opt-In program served as the primary data source for the CY 2024 capitation rate development for the MyCare Opt-In program. The following data sources were utilized to inform adjustments to the plan-submitted encounter data: ▪ Historical MyCare eligibility files provided by ODM; ▪ Annual and quarterly MyCare cost report data submitted by the MCOPs; ▪ Re-priced inpatient and outpatient hospital claims experience provided by ODM; ▪ Fee-for-service (FFS) data for dual eligibles; ▪ 2024 MCOP Survey submissions completed by each MCOP, which includes SFY 2022 claims and eligibility data (SFY 2022 cost report data); ▪ ODM fee schedules applicable to services affected by reimbursement changes due to legislative budget appropriations; and, ▪ Member-level enrollment data related to the PHE unwinding.
Types of Data. Data collected as evidence of teacher practice may be quantitative, qualitative, or a combination of both. Quantitative data includes frequencies, distributions and other counts or tallies. For example the observer could use a checklist to tally how many questions were asked of children in the front row or children who had their hands raised versus not. The evaluator might also chart the types of questions asked (higher versus lower levels). Qualitative data can include scripted notes detailing patterns of activities, vocabulary used, and events observed. In both cases accuracy is essential to ensure the credibility of the process and the evaluator.
Types of Data. The Personal Data Processed may concern for example the following types of Data of the above Data Subjects. ☒ Personal master data (customer-number, customer ID or national number or similar) ☒ Name, title, name suffix ☒ Personal telephone number, mobile phone number, e-mail address, fax number ☒business ☒private ☒ Personal address ☒business ☒private ☒ Date of birth/age ☒ Written correspondence or documentation (contract, offers, letters, faxes, messages, e-mails) ☒ Contractual data (contractual relationship with an individual person; an individual’s interest in a product or contract) ☒ Contract billing and payment data of an individual person ☒ Customer history of an individual person ☒ Personal data that fall in the category of “professional secret “/professional obligation to discretion (e.g., lawyers, doctors, workers council, data protection officers) ☒ Data relating to criminal activities, misdemeanors or offences of individual persons or the suspicion of such behavior ☒ Data about bank or credit card accounts of individual persons ☒ Financial data of individual persons ☒ Scoring data relating to individuals (e.g., obtained from scoring agencies) ☒ Photographs (identifiable persons) ☒ Data which allows the creation of a personal profile or tracking user behavior (e.g., Tracking Cookies, browsing history)