Data Preparation Clause Samples

Data Preparation. The Contractor approaches data preparation in a way that is ongoing, automated wherever feasible, scalable, and auditable. The Contractor’s preparation approach must be flexible and extensible to future data sources as well, including State datasets and systems. For the CCRS, data preparation will consist of the following at a minimum: 1. The ability to perform data matching, deduplication, cleaning, and other needed data processing across both current datasets and future State datasets for identified data. 2. Reports that monitor ongoing data preparation processes, including, for example, the success of data matching, de-duplication, and more (e.g., metadata). 3. Workflow for onboarding new datasets into the existing data preparation process. 4. Data preparation activities apply to both Phase 1 and Phase 2. 5. Volume Transaction fee is included as part of the monthly ODX and Diameter transaction fee up to 150,000 message per day. (please see Costing sheet line #13). 6. In excess of the 150,000 messages per day, additional Tiered pricing transaction fee applies and is available on the CA Costing sheet. The additional Transaction per day Fee is based on the:  Low and High message volume will be based off of average daily transaction for month.  Tier pricing does not apply to Phase 1 historical data conversion. 7. In excess of 150,000 messages per day, Optum will calculate the average daily transaction for the month and will provide a Work Order Authorization (WOA) document reporting the daily excess messages. CDPH will review the excess messages report and the associated tiered pricing. Approval of the excess message report will be provided through WAD by CDPH and will be used by Optum for invoicing.

Data Preparation. Data Cleaning and Coding

Data Preparation. HDM-4’s required input is organized into data sets that describe road networks, vehicle fleets, pavement preservation standards, traffic and speed flow patterns, and climate conditions. Most of the required pavement performance information was obtained from 2002 data within the Washington State Pavement Management System (WSPMS) (▇▇▇▇▇▇▇▇▇▇▇▇ et al., 2002). Other data were obtained through available literature and interviews with WSDOT personnel. used for HDM-4 input and included passenger cars, single-unit trucks, double-unit trucks, and truck trains (▇▇▇▇▇▇▇▇▇▇▇▇ et al., 2003). Specific inputs shown in Table 1 are not described in this report. Table 1: Maintenance standard of 45-mm HMA overlay in HDM-4 version 1.3 General Name: 45-mm HMA Overlay Short Code: 45 OVER Intervention Type: Responsive Design Surface Material: Asphalt Concrete Thickness: 45 mm Dry Season a: 0.44 CDS: 1 Intervention Responsive Criteria: Total cracked area ≥ 10% or Rutting ≥ 10 mm or IRI ≥ 3.5 m/km Min. Interval: 1 Max. Interval: 9999 Last Year: 2099 Max Roughness: 16 m/km Min ADT: 0 Max ADT: 500,000 Costs Overlay Economic: 19 dollars/m2 * Financial: 19 dollars/m2 * Economic: 47 dollars/m2 * Financial: 47 dollars/m2 * Economic: 47 dollars/m2 Financial: 47 dollars/m2 Effects Roughness: Use generalized bilinear model a0 = 0.5244 a1 = 0.5353 a2 = 0.5244 a3 = 0.5353 Rutting: Use rutting reset coefficient = 0 Texture Depth: Use default values (0.7 mm) Skid Resistance: Use default value (0.5 mm) A BST surface application is triggered when the total area of pavement cracking is ≥ 10 percent of the total roadway area. Table 2 lists the major inputs. Table 2: Maintenance standard of BST resurfacing in HDM-4 version 1.3 General Name: BST resurfacing Short Code: BSTCRA Intervention Type: Responsive Design Surface Material: Double Bituminous Surface Treatment Thickness: 12.5 mm Dry Season a: 0.2 CDS: 1 Intervention Responsive Criteria: total cracked area ≥ 10% Min. Interval: 1 Max. Interval: 100 Max Roughness: 16 m/km Max ADT: 100,000 BST Economic: 2.04 dollars/m2 * BST Financial: 2.04 dollars/m2 * Patching Economic: 47 dollars/m2 * Patching Financial: 47 dollars/m2 * Edge Repair Economic: 47 dollars/m2 * Edge Repair Financial: 47 dollars/m2 * Crack Seal Economic: 8.5 dollars/m2 * Crack Seal Financial: 8.5 dollars/m2 * Effects Roughness: Use user defined method Roughness: 2 m/km Mean rut depth: 0 mm Texture Depth: 0.7mm Skid Resistance: 0.5mm different road widths (narrow, standard, and wide)...

Data Preparation. All documents, instruments and data supplied by Client to TCS will be supplied in accordance with the previously agreed upon time requirements and specifications set forth in Schedule 1. Client shall be responsible for all consequences of its failure to supply TCS with accurate documents and data within prescribed time periods. Client agrees to retain duplicate copies of all documents, instruments and data supplied by Client to TCS hereunder; or, if the production and retention of such copies is not practical, Client holds TCS blameless for loss or damage to said documents. Client is responsible for the accuracy and completeness of its own information and documents and Client is responsible for all of its acts, omissions and representations pertaining to or contained in all such information or documents. Unless Client previously informs TCS in writing of exceptions or qualifications, TCS has the right to rely upon the accuracy and completeness of the information and documents provided by Client and TCS assumes no liability for services performed in reliance thereon. TCS shall inform Client of any erroneous, inaccurate or incomplete information or documents from the Client to the extent such becomes apparent or known to TCS. However, unless expressly accepted in writing as a part of the service to be performed, TCS shall have no obligation to audit or review Client's information or documents for accuracy or completeness.

Data Preparation. Well water test data was provided to Emory University by ARK starting in November 2019. The ARK dataset contained censored data consisting of values below their respective limits of detection (LODs) and missing data for variables that were not tested. Censored data consists of unknown values beyond a certain threshold. In this study, censored data refers to data points below the parameter’s LOD. These parameters resulted in little variation across observations due to their data points being below the limit of detection (LOD) and/or were not tested for throughout the duration of the sampling time period and therefore were not used in correlation or regression analysis. Censored data points were imputed using their respective LOD divided by the square root of 2, per EPA guidance [24].

Data Preparation. Esri will support the City with preparing the source data requested as part of Task 2. The prepared data will then be published as feature services to the City’s ArcGIS Online Organization (AGOL), enabling these services to be used and manipulated by ArcGIS Urban once it has been deployed. It is anticipated the following data preparation steps will be performed:  Reproject data to appropriate coordinate system.  Clean up parcel geometries using geoprocessing tools (repair geometry, generalize, multipart to single part, etc.).  Assign standard road classification to centerlines.  Assign parcel edge information.  Interpret zoning code parameters (e.g., floor area ratio [FAR], setbacks, heights, coverage) for up to 5 zones, 1 overlay, 5 current land uses, and 5 future land uses.  Prepare approximately 10 residential and nonresidential space uses and building types based on the development typologies identified in Task 2.  Load parcel, zoning, project, plan, and indicator geometries and attributes into the ArcGIS Urban data model.  Publish loaded layers as feature services to the City’s AGOL. Once all necessary feature services are published, Esri will support the City by conducting the following ArcGIS Urban deployment tasks:  Populate ArcGIS Urban configuration tables to read to the previously published services, including previously created services for existing 3D buildings.  Configure ArcGIS Online permissions, enabling specified groups and accounts to access the ArcGIS Urban Web application.  Configure the plan area, focused project, and up to four custom indicators identified during the project kickoff meeting and deployed to ArcGIS Urban. Esri anticipates configuration include tasks such as adding descriptions, URL links, charts, etc., to the deployed features using the Web-based interface.

Data Preparation. ‌ We manually selected 10 conversations from the CallHome Corpus based on their audio quality. Each conversation lasts around 30 minutes, but the reference transcript only covers 10 minutes of the audio. Therefore, we cut each of the audio into a 10- minute clip and transcribe it using Amazon and RevAI separately. The transcription output from both RevAI and Amazon Transcribe is provided in JSON format. For RevAI, the output consists of a list of monologues, with each monologue containing speaker information and a list of elements representing indi- vidual tokens, including text, punctuation, and timestamps. In contrast, Amazon’s output is structured with separate lists for transcripts, speaker labels, and items. The transcripts list contains the entire transcript as a single string, while the speaker labels list stores diarization results as speaker segments. The items list contains individual tokens with timestamps and confidence scores.

Data Preparation. The NREL team will communicate with SEA on the geographic extent of the microsimulation model and the data sources needed for enabling the master function. Besides the existing microsimulation model and the passenger demand profile, the NREL team will work with Port staff to estimate traffic volume entering the simulation area on major access roads, background traffic demand (e.g., recirculating traffic, employee commuting, etc.), bypassing traffic volume on major access roads, and the distribution of passenger origins and destinations inside the airport (e.g., terminals, curb segments, etc.). The NREL team will seek other open sources (such as TomTom API) for any inputs that are not currently available to the Port staff.‌

Data Preparation. The following subsections provide insight into the data issues involved when using the new version of HDM-4. 3.1.1: Version 1.3