Data Set Sample Clauses

Data Set. The MLS Data Set is copyrighted information concerning real estate which has been compiled by and belongs to MLSSAZ.
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Data Set. In this paper, we present the development of a training data set for Dutch Named Entity Recognition (NER) in the archaeology domain. This data set was created as there is a dire need for semantic search within archaeology, in order to allow archaeologists to find structured information in collections of Dutch excavation reports, currently totalling around 60,000 (658 million words) and growing rapidly. To guide this search task, NER is needed. We created rigorous annotation guidelines in an iterative process, then instructed five archaeology students to annotate a number of documents. The resulting data set contains roughly 31k annotations between six entity types (artefact, time period, place, context, species & material). The Inter Annotator Agreement (IAA) is 0.95, and when we used this data for machine learning, we observed an increase in F1 score from 0.51 to 0.70 in comparison to a machine learning model trained on a data set created in prior work. This indicates that the data is of high quality, and can confidently be used to train NER classifiers.
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Data Set. Covered Entity agrees to share the following data with Data User: [insert description, or include as an attachment] (the "Data Set"). Such Data Set shall not contain any of the following identifiers of the individual(s) who is(are) the subject(s) of the Protected Health Information, or of relatives, employers or household members of the individual(s): names; postal address information, other than town or city, state and zip code; telephone numbers; fax numbers; electronic mail addresses; social security numbers; medical record numbers; health plan beneficiary numbers; account numbers; certificate/license numbers; vehicle identifiers and serial numbers, including license plate numbers; device identifiers and serial numbers; Web Universal Resource Locators (URLs); Internet Protocol (IP) address numbers; biometric identifiers, including finger and voice prints; and full face photographic images and any comparable images.
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Data Set. The Data Requestor agrees to use and the Agency agrees to disclose the following Data Set, with the variables delineated in Exhibit “B”, to the Data Requestor for use by the Data Requestor in the performance of the Activities described above.
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Data Set. The data set that was used in this master’s thesis consisted of data from 2009 to 2017. It is important to state that the decision was made to focus on two years, in particular 2012 and 2013, since an analysis for each year would have been excessive. These years are not chosen randomly, since research has shown that re-election purposes are a crucial incentive for pork barrel politics. Therefore, this master’s thesis will examine the two years before the Flemish elections of 2014. The research question and hypotheses will thus be examined for both years individually and in the end, a comparison can be made. This third section is structured as follows, first the used method will be explained and second, the variables that were used in the analyses will be explained.
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Data Set. ‌ We used the Twitter Stream API, which provides a random sample comprising 1% of all tweets created in the world on a day. In order to form our collection, we collected all mes- sages provided by the API between January and June 2014 together with the available meta-information published in JSON format via the Stream API. This process yielded a collection of 784 million tweets from which 262 million are in English. Subsequently, we automatically selected 113K tweets (from 262 million English) on 10 controversial topics ”Obama”, ”Xxxx”, ”Xxxx Xxxx”, ”Xxxxxx Xxxxxx”, ”Islam”, ”Lakers”, ”Youtube”, ”iPad”, ”An- droid” and ”Microsoft”. Furthermore, we ended up with 17k tweets after applying following steps of Twitter Pre-Filtering component along with other steps
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Data Set. But this task only looks at common, general-domain entities and is not compa- rable to our data set (Xxxxx Xxx Xxxx, 2002). In the archaeology domain, NER data sets exist in other languages (English and Swedish), created in the ARIADNE project (Vlachidis et al., 2017). To our knowledge, the only directly related data set that deals with both Dutch and archaeological texts is another data set created in the same ARIADNE project, as briefly described in the introduction. As we are going to show in this paper, the data set we have created is of better quality and much larger than the ARIADNE data.
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Data Set. 2.1. The Client shall provide Xxxx Xxxx with such necessary documents, data and assistance relating to the Client’s trade data as agreed between Xxxx Xxxx and the Client (the “Data Set”) in order to enable Xxxx Xxxx to provide the Services on the terms of the agreement.
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Data Set. 2.1 The Data Controller holds two different data sets on households receiving benefits.
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Related to Data Set

  • Data Services In lieu of any other rates or discounts, the Customer will receive discounts ranging from 25% to 55% for the following Data Services: Access: Standard VBSII Guide local loop charges for DS-3 Network Services Local Access Services.

  • Data Security The Provider agrees to utilize administrative, physical, and technical safeguards designed to protect Student Data from unauthorized access, disclosure, acquisition, destruction, use, or modification. The Provider shall adhere to any applicable law relating to data security. The provider shall implement an adequate Cybersecurity Framework based on one of the nationally recognized standards set forth set forth in Exhibit “F”. Exclusions, variations, or exemptions to the identified Cybersecurity Framework must be detailed in an attachment to Exhibit “H”. Additionally, Provider may choose to further detail its security programs and measures that augment or are in addition to the Cybersecurity Framework in Exhibit “F”. Provider shall provide, in the Standard Schedule to the DPA, contact information of an employee who XXX may contact if there are any data security concerns or questions.

  • Data Segregation a. DSHS Data must be segregated or otherwise distinguishable from non-DSHS data. This is to ensure that when no longer needed by the Contractor, all DSHS Data can be identified for return or destruction. It also aids in determining whether DSHS Data has or may have been compromised in the event of a security breach. As such, one or more of the following methods will be used for data segregation.

  • Data Storage Where required by applicable law, Student Data shall be stored within the United States. Upon request of the LEA, Provider will provide a list of the locations where Student Data is stored.

  • Data Security and Privacy Plan As more fully described herein, throughout the term of the Master Agreement, Vendor will have a Data Security and Privacy Plan in place to protect the confidentiality, privacy and security of the Protected Data it receives from the District. Vendor’s Plan for protecting the District’s Protected Data includes, but is not limited to, its agreement to comply with the terms of the District’s Bill of Rights for Data Security and Privacy, a copy of which is set forth below and has been signed by the Vendor. Additional components of Vendor’s Data Security and Privacy Plan for protection of the District’s Protected Data throughout the term of the Master Agreement are as follows:

  • Data Encryption Contractor must encrypt all State data at rest and in transit, in compliance with FIPS Publication 140-2 or applicable law, regulation or rule, whichever is a higher standard. All encryption keys must be unique to State data. Contractor will secure and protect all encryption keys to State data. Encryption keys to State data will only be accessed by Contractor as necessary for performance of this Contract.

  • Data Subjects The categories of Data Subjects who we may collect Personal Data about may include the following where they are a natural person: the Customer, the directors and ultimate beneficial owner(s) of the Customer, your customers, employees and contractors, payers and payees. You may share with Airwallex some or all of the following types of Personal Data regarding Data Subjects: • full name; • email address; • phone number and other contact information; • date of birth; • nationality; • public information about the data subject; • other relevant verification or due diligence documentation as required under these terms; and • any other data that is necessary or relevant to carry out the Agreed Purposes.

  • Data Security and Privacy 12.1 SERVICE PROVIDER acknowledges the importance of Data Security and agrees to adhere to the Terms and Conditions of the Data Security Policy of IIMC.

  • Data Access Access to Contract and State Data The Contractor shall provide to the Client Agency access to any data, as defined in Conn. Gen Stat. Sec. 4e-1, concerning the Contract and the Client Agency that are in the possession or control of the Contractor upon demand and shall provide the data to the Client Agency in a format prescribed by the Client Agency and the State Auditors of Public Accounts at no additional cost.

  • Data Security and Unauthorized Data Release The Requester and Approved Users, including the Requester’s IT Director, acknowledge NIH’s expectation that they have reviewed and agree to manage the requested controlled-access dataset(s) and any Data Derivatives of controlled-access datasets according to NIH’s expectations set forth in the current NIH Security Best Practices for Controlled-Access Data Subject to the GDS Policy and the Requester’s IT security requirements and policies. The Requester, including the Requester’s IT Director, agree that the Requester’s IT security requirements and policies are sufficient to protect the confidentiality and integrity of the NIH controlled-access data entrusted to the Requester. If approved by NIH to use cloud computing for the proposed research project, as outlined in the Research and Cloud Computing Use Statements of the Data Access Request, the Requester acknowledges that the IT Director has reviewed and understands the cloud computing guidelines in the NIH Security Best Practices for Controlled-Access Data Subject to the NIH GDS Policy. The Requester and PI agree to notify the appropriate DAC(s) of any unauthorized data sharing, breaches of data security, or inadvertent data releases that may compromise data confidentiality within 24 hours of when the incident is identified. As permitted by law, notifications should include any known information regarding the incident and a general description of the activities or process in place to define and remediate the situation fully. Within 3 business days of the DAC notification, the Requester agrees to submit to the DAC(s) a detailed written report including the date and nature of the event, actions taken or to be taken to remediate the issue(s), and plans or processes developed to prevent further problems, including specific information on timelines anticipated for action. The Requester agrees to provide documentation verifying that the remediation plans have been implemented. Repeated violations or unresponsiveness to NIH requests may result in further compliance measures affecting the Requester. All notifications and written reports of data security incidents and policy compliance violations should be sent to the DAC(s) indicated in the Addendum to this Agreement. NIH, or another entity designated by NIH may, as permitted by law, also investigate any data security incident or policy violation. Approved Users and their associates agree to support such investigations and provide information, within the limits of applicable local, state, tribal, and federal laws and regulations. In addition, Requester and Approved Users agree to work with the NIH to assure that plans and procedures that are developed to address identified problems are mutually acceptable and consistent with applicable law.

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