Data Summary Sample Clauses

Data Summary. The main categories of data foreseen to be collected or generated by MULTI-STR3AM are: • Underlying research data: This category encompasses the data, including associated metadata, forming the basis of results and conclusions presented in scientific articles and in any potential patents arising from the project. To remove any limitations to review and validation of results by the scientific community, green open access (self-archiving) will be the preferred model of publication for scientific articles. Additionally, the underlying data will be deposited in an open repository (independent of the project), which will be linked to in the resulting article. • Operational data: This includes raw or curated data arising from the operation of equipment, for example associated with biomass cultivation, fractionation and purification of microalgae components, and routine analyses of the resultant products (e.g., compositional analyses). Data related to the production process will be used to produce guidelines for optimal performances, quality checks and confirmation checks, which will be of use in the project and in future planned production of algae. This category of data is likely to contain commercially sensitive data; careful consideration will be given to which information can be published openly (e.g., for dissemination purposes) and which should be consideration non-open. Some of this data is also of value for scientific or other publications and presentations and will be treated accordingly. • Impact monitoring data: Primarily in WP5, data will be gathered to assess the social, environmental and economic impact of MULTI-STR3AM and to track the performance of the project against the KPIs set out in the proposal. These data include biorefinery process modelling and data gathered on e.g., feedstock, raw materials, energy, waste and emissions to complete life cycle and social life cycle assessments. Such assessments will be performed according to methodology as defined by ISO 14040/44 and the project impacts measured with the help of computer-based tools such as SimaPro v9 (with Ecoinvent v3.5 database, and others). • Documentation relating to instruments and methods: This category covers documentation needed to implement the project and reproduce its results, including SOPs from each partner for their respective processes and details of tools, methods, instruments and software. This section will describe the kinds of data that each work package will be handli...

Data Summary. In summary, we see that the existence of patterns in which a single conjunct controls (some) agree- ment processes considerably complicates the array of possible strategies for syntactic agreement with (nominal) coordinate structures. In addition to (18-1) and (18-2) we must accommodate a number of further patterns. (18) 1. Agreement with resolved coordination-level features

Data Summary. The following references furnish data to be incorporated in the specified Sections of this Lease and shall be construed to incorporate the entire Section:

Data Summary. The main purpose for the data collection/generation of the CarE-Service project is for the description of new circular economy business models in innovative hybrid and electric mobility through advanced reuse and remanufacturing technologies and services. The CarE-Service project will produce several datasets during the lifetime of the project. All the data which will be collected will be relevant to the purposes of the projects, such as the establishment of circular economy business models, the development of the Smart Mobile Modules, the creation of customer-driven products and the development and validation of technical solutions for reused, remanufactured and recycled components and the evaluation of these business models through demonstration and life cycle assessment (LCA). All the collected or generated data will be analyzed and evaluated from a range of methodological perspectives for project development and engineering and scientific purposes. A range of data will be created during the project. These will be available in a variety of easily accessible formats, including Documents (Word) (DOCX), Spreadsheets (Excel) (XLSX, CSV), Presentation files (Power Point) (PPT), PostScript (PDF, XPS), images, audio and video files (JPEG, PNG, GIF, TIFF, WAV, MPEG, AIFF, OGG, AVI, MP4), Technical CAD drawings (DWG), Origin (OPJ), compressed formats (TAR.GZ, MTZ), Program database (PDB, DBS, MDF, NDF), etc. (see Table 5-1). As no comparable data are available for secondary analysis at the moment, it is planned to make our dataset publicly available in a research data repository. Apart from the research team, the dataset will be useful for other research groups working on eco-innovative circular economy business models on large scale demonstration projects. The following table contains all the datasets that will be generated during the project. The expected size of the datasets produced will be between 5MB and 1GB. For every dataset which will be generated for a task, the leading partner of the task will be the Master of Data. The Master of Data will be responsible for the collection of the data from the other partners, the file and sharing actions among the consortium, the creation of the linked metadata files and also the activities for the publish of the data, e.g. on Zenodo platform. Table 5-1: Potential Datasets Potential datasets – Description Format Dissemi- nation level Master of Data 1 Task 1.1.a Survey for deriving detailed information about mobility u...

Data Summary. 2.1 Purpose of data collection and generation 2.2 Types and formats of data 2.2.1 Types and formats of research data collected in the project. Clinical data from HGSOC patients Sequencing data Imaging data Measurement data from experiments and analyses Figure 1. Workflow for calling germline short variants from whole genome sequencing data. 2.2.2 Data collected or generated for project management

Data Summary. In order to provide an overview of the different datasets that are produced over HECARRUS project life cycle, Table 2 presents the details of the data type, origin and format extension. Data types include numerical datasets, computer codes, text data, technical figures, contact lists, survey and workshops data. Primary data correspond to the main output that undergoes the already described confidentiality control, before it is made publicly available. Table 2. Information on the data types that will be used within the project.

Data Summary. 2.1. State the purpose of the data collection/generation 2.2. Explain the relation to the objectives of the project 2.3. Specify the types and formats of data generated/collected

Data Summary. ‌ The following table summarizes the data collected for each survey date during season 1 of the ▇▇▇▇▇ bald eagle night roost surveys. Section 6 of this report provides detailed data forms and observation maps. Table 1. Bald eagle night roost data summary. Survey Date Total bald eagle observations Number of bald eagles perching Behavior Summary

Data Summary. ‌ 1.4.1 The purpose of the data generation and collection in TRIP‌ The aim of the data generation and collection in TRIP is to develop a high-risk clinic for pancreatic cancer, including patients with pancreatic cystic lesions, and a pilot programme for pancreatic cancer screening. A screening algorithm for pancreatic cancer will be proposed for UMFCD and its affiliated hospitals, based on population-specific data and available infrastructure. 1.4.2 The relation to the objectives of the project‌ Data generation and collection will support the main objectives of the project – improving knowledge and expertise for early diagnosis and minimally invasive therapy of bilio- pancreatic cancers, and developing a high-risk clinic and pancreatic cancer screening programme. 1.4.3 The types and formats of data generated/collected and re-used‌ Several types of data will be generated/collected and re-used in fulfilling the project objectives – personal data of participants who will take part in training programmes (workshops, webinars, summer schools), and demographic, clinical, laboratory and imaging data from patients recruited for pancreatic cancer screening programme, high-risk clinic and pancreatic organoids cultures. Data formats include documents, spreadsheets and presentations in software such as MS Office (doc, docx, xls, xlsx, ppt, pptx, etc.), photos (png, jpg, jpeg) and videos (avi, mpg, DICOM). 1.4.4 Expected size of the data that you intend to generate or re-use?‌ While most data will be text, considering that we will also generate and collect photo and video-data, we estimate the need for hundreds of gigabytes – few terabytes. 1.4.5 Origin/provenance of the data, either generated or re-used‌ Regarding the provenance of data, on one hand we will have personal data of participants to training activities, collected by sign-up sheets, and on the other hand, there will be patient data, collected from medical charts and medical equipment.