Data Limitations Sample Clauses

Data Limitations. Unless specifically agreed to in writing by a duly authorized officer of ExtraHop, Customer agrees not to transmit or store within ExtraHop SaaS (a) any Protected Health Information, as defined in the Health Insurance Portability and Accountability Act of 1996, as amended and supplemented by the Health Information Technology for Economic and Clinical Health Act,

Data Limitations. Trial period offering will not be limited by the amount of data the customer requires to be backed up (GBs) without prior agreement from HP.

Data Limitations. In connection with accessing the granting Party’s Personal Information for a use permitted under Section 2.2 above, the accessing party shall not access any of the following data relating to the granting Party’s customer: (i) any data, record, or file that could be deemed a consumer report or credit score as those terms are defined in Sections 603 and 609 of the Fair Credit Reporting Act (15 U.S.C. §§ 1681a, 1681g) or other similar Laws; (ii) any data relating to a specific customer(s) that the granting Party identifies as a customer(s) with respect to which access is not permitted; or (iii) other specific data elements identified by the granting Party from time to time. In order to facilitate compliance with these limitations, each Party shall identify those data elements or customers within its customer database that are covered by these limitations via a flag or similar notation and will provide at least ten days’ notice before modifying the list of specific data elements to which this clause applies.

Data Limitations. Unless specifically agreed to in writing by a duly authorized officer of ExtraHop, Customer agrees not to transmit or store within Cloud Services any health information, financial or payment card information, information that is protected by the Internal Traffic in Arms Regulation, or matter, data, or information that is export-controlled.

Data Limitations. Company will impose the following data limitations on email and bandwidth provided by Company through its products and Services offering.

Data Limitations. Unless specifically agreed to in writing by a duly authorized officer of MistNet, Customer agrees not to transmit or store within MistNet SaaS (a) any Protected Health Information, as defined in the Health Insurance Portability and Accountability Act of 1996, as amended and supplemented by the Health Information Technology for Economic and Clinical Health Act, (b) any financial or payment card information, including information protected under the Xxxxx-Xxxxx-Xxxxxx Act, (c) any information that is protected by the Internal Traffic in Arms Regulation, or (d) any matter, data, or information that is export-controlled.

Data Limitations. The County makes no warranty, expressed or implied, concerning the imagery products’ content, accuracy, currency or completeness, or concerning the results to be obtained from queries or use of the data. All imagery products are expressly provided “As Is” and with all faults. The County makes no warranty of fitness for a particular purpose and no representation as to the quality of any products. No employee or agent of the County or the Customer is authorized to waive or modify this paragraph or make any representations or provide any warranties, expressed or implied, concerning the imagery products.

Data Limitations. In Iceland, there were over 8,650 confirmed cases of ILI during the 2009 flu pandemic [23]. The ILI-­‐onset dataset contains 4,346 records, which is roughly half of confirmed ILI cases. In addition, we can assume that many people who contracted H1N1 did not go to the doctor, and thus were not recorded in the ILI-­‐onset dataset. In fact, the estimated proportion of the population that was infected ranges from 10% to 22% [24], though it appears to be 3% if we look at just the confirmed cases. These infected people provided a vector of transmission that the models cannot capture. This relates to the second limitation that the CDR-­‐dataset does not have data on every person in Iceland, so those missing data points could act as indirect vectors of transmission. There is a similar problem with the ILI-­‐onset dataset, namely, not all infected individuals are represented. Infected individuals may neglect to go to the doctor and thus are never accounted for. That being said, infectious individuals who did not see the doctor, because they are asymptomatic are not as important to disease spread as are symptomatic individuals [25]. The coordinates in the CDR dataset represent the location of the cell-­‐phone towers, so we do not know the exact location where the users were making calls or sending text messages from. The accuracy of the approximation depends on the density of cellphone towers in the region. The CDR dataset has no information on who a user texted. Considering the popularity of texting especially among teenagers, we lose out on the ability to use text messaging frequency and patterns to infer social strength among users. Cell phone tower switches are not captured in the data. If a user initiates a call and then during the call connects to another cell phone tower, the data does not show those towers. This may be more prevalent when people are driving, but since it is unlikely that people transmit the disease to other people on the road, the missing data points are not hindrances. Considering that the population of Iceland in 2010 was around 320,000 [26], and there are over one million unique UIDs in the CDR dataset, many of these UIDs may represent not individuals but businesses. Also, phone ownership in Iceland in 2010 was at 107% [27] and some users own multiple phones. Currently, we have no reliable way of cleaning the CDR dataset. The data we have only represents the adult population (at least 18 years of age). Knowing the role schools played in H...

Data Limitations. For this process of content coding, DTSC is relying on data collected electronically and inputted directly into the Collector field application. The process of collecting electronic field data took effect in November 2016. DTSC is not relying on previously acquired handwritten field notes for this process of content coding as the data is more difficult to consolidate to APN specific data. Based on field observations, we estimate that every single APN in the PIA was reached 5+ times prior to the electronic collection of field notes. Field data was electornically collected by 50+ different individuals, therefore there is a wide variation of styles of data gathering that are evident in the dataset. //Ref_M.D.YY _II // Property Owner -- verbal confirmation -- does not want to sign "said no" 2+ attempts from tenant to speak to property owner "said doesn't want to sign" "has said no before" //Unres_M.D.YY _II // 3+ attempts to make contact at the property -- as recorded on collector "Left Materials" Field notes categorize that resident chose to forego outreach opportunity "inside property/no answer" 2+ attempts from tenant to speak to property owner "spoke to owner" Note to indicate: Vacancy at the property / dilapidated structures //Disen_M.D.YY _II // Field notes describe disengagement with the project "not interested" 3 weeks after field notes record materials being handed directly to a person and asked to mail back "left materials" No recorded increase of interest "mail back" "here before"