D2.2 Data register, feature report and accreditation

The PHOEBE Framework is a methodological approach designed for cities to improve understanding of the safety implications of future changes in the transport systems, such as behavioural changes, redesign or new infrastructure, or evolving modes of travel. To do so, this requires evidential data and proven approaches. This document details the use of data in the PHOEBE framework. This is supported by: 1. An account of the purpose of this document. 2. A review of the project data including: a. A review of prior project data registers and the need for public facing registers. b. A documented summary of the D2.2 live document public registers related to data. This includes: i. Data resource and FAIR register, detailing the aim and structure of this resource. ii. Features framework register, detailing the aim and structure of this resource. 3. A review of AI usage in the project, including: a. Summary of management controls and processes for AI management in the project. b. Extensive details on the usage of AI and its management for compliance. 4. The use of accreditation approaches to ensure new data sources meet the required standards of accuracy and value. Ultimately, this report summarises the use of data, AI and accreditation in the PHOEBE project from a data management perspective. This document is supported by public registers published at https://doi.org/10.5281/zenodo.16737146.

Institutions

Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-04-30
DOI
https://doi.org/10.5281/zenodo.17206476
Primary Topic
Agriculture Sustainability and Environmental Impact
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

D2.2 Data register, feature report and accreditation

Zenodo (CERN European Organization for Nuclear Research)
Agriculture Sustainability and Environmental Impact
article

D2.2 Data register, feature report and accreditation

article en

Abstract

The PHOEBE Framework is a methodological approach designed for cities to improve understanding of the safety implications of future changes in the transport systems, such as behavioural changes, redesign or new infrastructure, or evolving modes of travel. To do so, this requires evidential data and proven approaches. This document details the use of data in the PHOEBE framework. This is supported by: 1. An account of the purpose of this document. 2. A review of the project data including: a. A review of prior project data registers and the need for public facing registers. b. A documented summary of the D2.2 live document public registers related to data. This includes: i. Data resource and FAIR register, detailing the aim and structure of this resource. ii. Features framework register, detailing the aim and structure of this resource. 3. A review of AI usage in the project, including: a. Summary of management controls and processes for AI management in the project. b. Extensive details on the usage of AI and its management for compliance. 4. The use of accreditation approaches to ensure new data sources meet the required standards of accuracy and value. Ultimately, this report summarises the use of data, AI and accreditation in the PHOEBE project from a data management perspective. This document is supported by public registers published at https://doi.org/10.5281/zenodo.16737146.

Zenodo (CERN European Organization for Nuclear Research)
Daewoo Pharma (South Korea) (KR)
Industry, innovation and infrastructure
Openalex Percentile: Top 43%
Agriculture Sustainability and Environmental Impact
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

D2.2 Data register, feature report and accreditation · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS