Data Set. Oracle shall not be liable for any loss whatsoever arising from or in connection with the Client's interpretation of the Data Set or Deliverables and/or the consequences of any action taken by the Client based on any Data Set or Deliverables. The Client acknowledges that the Data Set or Deliverables have been created and delivered with the mutual understanding that, if the Client requires additional protection or coverage, the Client should procure separate insurance.
Data Set. The MLS Data Set is copyrighted information concerning real estate which has been compiled by and belongs to MLSSAZ.
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.
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.
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.
Data Set. 2.1 The Data Controller holds two different data sets on households receiving benefits.
a) The Single Housing Benefit Extract (SHBE) which includes information on all households in receipt of Housing Benefit and is generated each month. B) A data set on households receiving council tax support (i.e. CTR605, CTS Extract and CTR300 if available).
2.2 The Data Controller will supply the Data Processor with a version of each data set, extracted on the same date.
2.2.1 Extracting the data is typically straightforward for the relevant person within the councils. Guidance on how to extract the data is detailed below, additional guidance may be provided by Policy in Practice:
2.2.2 The extract will maintain all data fields within the header row, in order to facilitate efficient and accurate processing of the data. Redacted data will be replaced with null values in the relevant fields by the data controller.
2.3 A redacted version of these extracts will be made available to Policy In Practice Ltd in order that individuals are not identifiable. To enable the Data Processor to undertake their analysis the data provided will include details of where the property is (postcode) and dates of birth and what the particular circumstances of an individual claimant are so that they are able to plot how the changes in the scheme will affect them.
2.4 The following record types from the SHBE/CTR file will be provided: All record types
2.5 All data from the following fields will be blanked out prior to supplying the SHBE file: Fields 4-7 inclusive Fields 127-129 inclusive Fields 279-283 inclusive Fields 288-289 inclusive Fields 300-307 inclusive Fields 313-314
2.6 For Northgate sites, the following fields of data from the CTR605 file(or Equivalent CTS Data) will be provided: recordtype; subrecordtype; claimreference; subrecorddob; ndgrossweeklyincome; ndstatus; ndirbenefit; partnerflag; cldob; clgender; numberofdp; ptdob; ptgender; clcaincome; ptcaincome; clesacincome; ptesacincome; clesairincome; ptesairincome; clesawrincome; ptesawrincome; clesascincome; ptesascincome; clwdpincome; ptwdpincome; clwwpincome; ptwwpincome; claaincome; ptaaincome; numberofnd; counciltaxband; weeklyctaxliab; cldlacincome; ptdlacincome; cldlacmincome; ptdlacmincome; cldlachincome; ptdlachincome; cldlamincome; ptdlamincome; clcapital; ptcapital; clsdaincome; ptsdaincome; clunearnedincome; ptunearnedincome; clearnedincome; clhoursofremunerativework; ptearnedincome; pthoursofremunerativework; clwtcinco...
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.
2.2. Xxxx Xxxx agrees to (a) use or disclose the Data Set only as permitted by this agreement or as required by law; (b) use all necessary and appropriate safeguards to prevent disclosure of the Data Set other than as permitted by this agreement or as required by law; (c) report to the Client any disclosure of the Data Set of which it becomes aware that is not permitted by this agreement or required by law; and (d) require any of its subcontractors or agents that receive or have access to the Data Set to agree to the same restrictions and conditions on the use and/or disclosure of the Data Set that apply to Xxxx Xxxx under this agreement. The Client agrees that the delivery of the Data Set to Xxxx Xxxx under this Section constitutes a non-exclusive, royalty-free, perpetual, irrevocable, and fully transferable and sub-licensable right for Xxxx Xxxx to use the Data Set. The term "use" of the Data Set in this Section 2.2 shall include the right to reproduce, distribute, perform and any other act or practice including, without limitation, masking, aggregation, accumulation, compilation, disassembly, reassembly, modification, creation and disclosure of derivative products from the Data Set and/or any other act, together with the use of other data sets and/or other data or information gathered or obtained by Xxxx Xxxx, which are necessary or incidental in order to provide the Services. A non-limiting example of such “use” is Grey Jean’s aggregation of the Data Set, together with similar data from other Xxxx Xxxx clients, in a non-attributable manner, to create a database of non-client specific information to be used in providing the Services to Client and other Xxxx Xxxx clients (the “Xxxx Xxxx Derivative Product”), including disclosure of the same or Grey Jean’s analysis based thereon.
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.
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.
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
1. Length based and re-tweets removal
2. Duplicate removal using MinHash alogorithm
3. Application of tweet spam classifier
4. Restriction to hashtags where the number of tweets is at least 10 Step 1, 2 and 3 yielded in 37k tweets, while step 4 further reduced the number of tweet instances to 17k.
