Suchergebnisse

Deliverable 1.1 – Data Management Plan (DMP) is related to WP1 – Coordination and management, Task 1.4 - Data protection, data management plan and ethics compliance. Task 1.4 runs for the entire duration of the project to ensure that the implementation of reCreating Europe adheres to the EU legal and normative framework, as well as to key ethical and societal values and principles. ReCreating Europe's Compliance Framework (CF) is developed as a structured set of guidelines covering the broadest possible spectrum of legal and ethical issues arising during the development of the project, considering, in particular, the rights of all relevant stakeholders and actors, both as end-users and as participants in surveys, workshops, and semistructured interviews. This DMP records the procedures for data management within the reCreating Europe project. It documents the types of data the project will generate or collect, the standards and platforms that will be used for storage and processing, measures to assure legal compliance, and the plan for how these data will be curated and preserved to enable maximal future exploitation, sharing and re-use. It represents the general strategy of reCreating Europe Consortium to establish a FAIR ecosystem on research data developed during the project in order to fulfil the Open Research Data Pilot policy. It has been already identified that several project's research activities will involve human participants, some of them being vulnerable, and the collection and processing of personal data will require the collection of informed consent and ethics clearance. Informed consent and ethical approval procedures and forms are included in D1.2 Informed consent forms and information sheets, to be read in conjunction with this document. This document provides in attachment a detailed list of datasets to be developed in each WP and specifies the rules to be applied for each research data category, in order to identify common rules on how data will be curated and preserved in the long term, and ...

The aim of WASTE4THINK ́s Data Management Plan is to provide an analysis of the main elements of the data management policy that are going to be used by the consortium. The results of the project waste4think It will produce an improvement in the management of public services which address the citizens and other stakeholders ́ needs. These services are expected to be based on the combination of Open Government Datasets with user-generated data though social networks and third party data to give place to added value datasets. The Waste4think project's partners are committed to offer as much information as possible generated by the project through Open Access. Such information includes: scientific publications issued by the project consortium, white papers published, open source code generated, anonymous interview results, or datasets used for gathering stakeholders ́ feedback. The present document constitutes the first version of WASTE4THINK`s Data Management Plan (DMP). The main objective of this DMP is to provide an analysis of the main elements of the data management policy that are going to be used by the consortium.

This policy details how the NT AHKPI system and data will be managed, maintained and protected in strict accordance with national and NT information privacy legislation and standards. ; Y

The changing financial services landscape -- Taxonomy of financial data -- Information as the fuel for financial services' business processes -- Challenges and trends in the financial data management agenda -- Data management tools and techniques -- Data management processes and quality management -- Data management organization -- What's next?

Aspects of Data Management

The FRAMES (Fibre Reinforced thermoplAstics Manufacturing for stiffEned, complex, double curved Structures) project, supported by Clean Sky 2 Joint Undertaking and the DLR, intend to develop advanced knowledge and manufacturing solutions for a full scale thermoplastic aircraft rear end. The project is part of the Clean Sky 2 initiative focused on developing concepts and enabling technologies for an optimum rear fuselage an empennage. FRAMES main objective is to validate and assess a manufacturing approach of an integral thermoplastic rear end with critical design features. Key technologies developed within FRAMES will be used into a mid-scale advanced rear end demonstrator manufactured by the Deutsches Zentrum für Luft- und Raumfahrt (DLR), part of a Clean Sky 2 technology platform for large passenger aircrafts. Project was launched on July 2020 and targets to deliver a xenon heating device simulation model for thermoplastic composites fiber placement, efficient double curved TP-stiffeners manufacturing solution and associated prototypes as well as a self-heating tooling equipment able to perform a coconsolidation of the skin and stiffeners in one shot. The Data Management Plan describes the data management life cycle for the data to be collected, processed and generated by the FRAMES project. FRAMES DMP is intended to be a living document to be updated throughout the project with generated results. To ensure accessibility of the data, FRAMES DMP applies through the whole duration of activities until the last update of data.

The Deliverable 1.2 is the Data Management Plan of the PIE News project. It aims at clarifying the procedures team members follow to collect, manage, and make publicly available the various types of data collected and generated through the project activities. It intends to provide team members with a complete guide on data management, with particular respect to privacy and data protection, on the one hand, and, on the other hand, to the Open Research Data pilot and how to make data F.A.I.R. Guidelines will be updated every time relevant changes in legislations or in the project development will arise.

The data management plan (DMP) covers the principles and protocols for data collection, processing, sharing and storage. The DMP will ensure that data management is compliant with the EU's Guidelines on Findable, Accessible, Interoperable and Reusable (FAIR) Data Management in Horizon 2020, and with relevant national data protection laws and institutional data management policies.

In recent years, the academic research data management (RDM) community has worked closely with funding agencies, university administrators, and researchers to develop best practices for RDM. The RDM community, however, has spent relatively little time exploring best practices used in non-academic environments (industry, government, etc.) for management, preservation, and sharing of data. In this poster, we present the results of a project wherein we approached a number of non-academic corporations and institutions to discuss how data is managed in those organizations and discern what the academic RDM community could learn from non-academic RDM practices. We conducted interviews with 10-20 companies including tech companies, government agencies, and consumer retail corporations. We present the results in the form of user stories, common themes from interviews, and summaries of areas where the RDM community might benefit from further understanding of non-academic data management practices.

In recent years, the academic research data management (RDM) community has worked closely with funding agencies, university administrators, and researchers to develop best practices for RDM. The RDM community, however, has spent relatively little time exploring best practices used in non-academic environments (industry, government, etc.) for management, preservation, and sharing of data. In this poster, we present the results of a project wherein we approached a number of non-academic corporations and institutions to discuss how data is managed in those organizations and discern what the academic RDM community could learn from non-academic RDM practices. We conducted interviews with 10-20 companies including tech companies, government agencies, and consumer retail corporations. We present the results in the form of user stories, common themes from interviews, and summaries of areas where the RDM community might benefit from further understanding of non-academic data management practices.