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. Questions Answers

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. State the purpose of the data collection/generation

Data Summary. This section gives, at project and individual level, an overview about the research data which is generated, collected, processed and stored during the MIP-Frontiers project. This includes the data description for different types and formats, purpose with respect to project objectives and tasks, preparation for data re-use, data origin, expected data size, and to whom it might be useful. Data generated in MIP-Frontiers should be strictly digital. In general, the data file formats to be used shall meet the following criteria: • widely used and accepted as best practice within the specific discipline, • self-documenting, i.e. the digital file itself can include useful metadata, • independent from specific platforms, hardware or software. At the level of corpora/datasets there are open initiatives that MIP-Frontiers will use; Jamendo, Freesound and AcousticBrainz are maintained by partners of this proposal (will be used by UPF1, UPF2, UPF3). For other projects the industrial partners will make available data (DRM Score Cloud Song database will be used for QMUL1 and QMUL2). At the level of software tools with which to extract musical features from audio recordings, Essentia, developed at the UPF, and Sonic Visualiser / Annotator, developed at QMUL are open tools that will be used and further developed.

Data Summary. ‌ Data collection will be generated in the frame of the research cruises funded through the ARICE project on board the six ARICE Research Icebreakers: In this frame, the cruises funded through ARICE will generate a variety of data which could include: 1. Atmospheric parameters: • Air temperature; • Wind speed and direction; • Air pressure; • Water vapor or humidity; • Precipitation; • Cloud fraction or cloud base height; • CO2, methane or other greenhouse gases; • Ozone or aerosols; • Radiation budget.

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. 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 overall objective of CIRCuIT is to demonstrate innovative solutions for closing the loop of urban materials and resource flows in the built environment sector. The aim is that these solutions will support a transformation of cities into centres of circular innovation, and support and increase the regenerative capacity of each city. In CIRCuIT the work packages generate data for different purposes: • Map flows of building materials in the four cities using mass-scanning approaches and blockchain technology, alongside existing building datasets to support digital pre- demolition audits and matching of supply and demand, and to overcome the barriers of data interoperability and availability (WP3). • Implement a cross-European and interdisciplinary Circularity Hub, as a data platform and one-stop-shop for evaluating progress of circular economy and regenerative capacity in urban and peri-urban areas of cities, including a range of indicators for monitoring this within the built environment (WP8). • The data from demonstrations generated in WP4-6 will be utilised in WP7 to analyse existing European, national and local regulations and procedures, to identify the room for manoeuvre that the four cities have for including requirements on the reuse and recycling of building products and materials, adaptive reuse and refurbishment, and design for disassembly in urban planning (spatial, municipal and local) and building permits. • Results are disseminated via WP9.

Data Summary. The main purpose of the Data Management Plan (DMP) is to describe the data management life cycle for the data to be collected, processed and/or generated by the ORP project. It also aims to provide a framework to support the European Commission’s goals for Open Access regarding publications, scientific and technical results and raw data resulting from activity supported by the XX Xxxxx Agreement. It is a requirement of Horizon 2020 grants that publications resulting from the grant should be made in an Open Access journal unless there are compelling reasons why this should not be done. Outputs from the ORP activities may be grouped into a number of different types: 1/ Scientific data resulting from the Transnational Access programme 2/ Scientific publications resulting from the Transnational Access programme 3/ Technological or software research and development. 4/ Technical data and publications resulting from Management and JA 1-2-3-4 5/ Technical and personal data resulting from the CTAC As part of making research data findable, accessible, interoperable and re-usable (FAIR), the DMP will identify for each category: ● Data set reference and name: identification of what data will be collected, processed and/or generated ● Data set description: description of the data set ● Standards and metadata: explanation of the methodology and standards that will be applied ● Data sharing: specify whether data will be shared/made open access or not. How will data be exploited and/or shared/made accessible for verification and re-use? If data cannot be made available, explain why. ● Archiving and preservation (including storage and backup): explanation of how data will be curated and preserved (including after the end of the project) The data collected and generated by the ORP project will be useful for astronomers, universities, science students, etc. By sharing the data, the project will contribute to additional scientific discoveries by re-use of data taken for other purposes.