Do ponto de vista de enquadramento institucional, como funcionário sem atribuições específicas, formais, no domínio em causa - disponibilização de dados e metadados (produzidos na parte da instituição) e gestão de acessos aos mesmos - não me sinto autorizado (e não o estou formalmente, ninguém com autoridade para tal me incubiu dessa tarefa) para responder pela instituição IST. Mais, ninguém no IST, com ou sem capacidade (e conhecimento) para tal me deu qualquer informação, treino ou formação sobre as (eventuais) políticas do IST (e a sua implementação) a esse propósito. Poderia responder - o exercício abaixo exemplifica-o no conjunto das 25 questões do inquérito - mas seria sempre, do meu ponto de vista, de forma insuficiente, insatisfatória, porque 1) não informada (desconheço o que se passa, se se passa, no IST, no que respeita a este assunto), com maioria de "Not sure" (não dá boa imagem da instituição); 2) limitada (apenas relativa ao LATR, mais concretamente à minha visão sobre o assunto, trabalho e implementação correspondentes, desenquadrado de qualquer política ou programa interno); 3) desenquadrada (não tem qualquer enquadramento ou ligação com qualquer programa que exista no IST, embora em sede de formulário apareça necessariamente a inserção na instituição. Este formulário não inclui campo algum que permita esclarecer o tipo de enquadramento, pressupõe - naturalmente - que a resposta é dada a nívelinstitucional, representativo, por alguém para tal qualificado, e.g. da área das TI e/ou com formação neste domínio específico). Poderia fazê-lo pelo LATR (embora nada haja de formalmente estabelecido, internamente, também nesse caso), de forma algo limitada já que muitas das questões terão necessariamente por resposta "Working on it" (na inexistência de um programa, planeamento ou enquadramento adequado, não pode dar-se uma resposta mais assertiva, "Yes", ou, em alguns casos "No"). Por estas razões, sem se discutir o assunto e esclarecer a situação de enquadramento, eu não responderei a este inquérito. Porém estou disponível para debater o assunto e ajudar.

IST [LATR/IST]

The questions delve into open data policies, support mechanisms, data management plan compliance, FAIR data training, policy review cycles, and the presence of workflows and tools. These aspects are crucial for establishing a robust foundation for open and accessible research data within the project.
2.Open data policy: Does your facility have an established open data policy in accordance with the FAIR data principles?.
An open data policy[1] is a set of guidelines and standards to ensure that research data is made available in an open and accessible manner. The FAIR principles[2] (Findable, Accessible, Interoperable, and Reusable) provide a framework for making data more accessible and usable for others.
For more information, see EU’s open science policy[3], and for an example, see CERN Open Data Policy for the LHC Experiments[4].

3.Data management plan compliance: Has your facility developed a data management plan compliant with ReMade@ARI’s requirements?
A compliant data management plan is a prerequisite for effective data stewardship within the ReMade@ARI project. See the ReMade Data Management Plan (DMP) for a reference [1].
[1]: https://cloud.hzdr.de/s/2TkRsWExWSyP59b - password: ReMade

4.FAIR data training: Has your facility provided specific training on FAIR data principles to researchers and staff?
Training in FAIR principles is critical to ensure that all participants understand how to make their data FAIR.

5.Policy review cycle: Does your facility have a regular review cycle in place for updating its data management policy to adapt to new insights and guidelines?
Regular reviews help institutions to stay current with evolving Open Science practices.

The questions in this section ask about the standardized data formats, metadata schemas, repository selection, persistent identifiers, repository compatibility with EOSC, metadata catalog integration, assessing data standards, and migrating legacy data to common standards. These practices ensure the long-term accessibility and usability of research data in ReMade@ARI.
6.Persistent identifiers (PIDs): Have persistent identifiers been implemented for published data assets?
Do researchers assign persistent identifiers, such as DOIs (Digital Object Identifiers), to their published data assets?
7.Assessing (meta)data standards: Is there a plan to assess, validate and promote the adoption of appropriate data and metadata standards?
This is crucial for ensuring the long-term accessibility, interoperability, and reusability of research data. This task involves, for example, identifying the relevant data and metadata standards for the type of data being collected and managed, implementing pilot testing of the selected standards to ensure their effectiveness in practices, and developing clear and concise documentation for the standards, including tutorials, examples, and reference materials.
8.Standardized data formats: Has your facility adopted standardized data formats for research data?
The use of standardized formats promotes interoperability and long-term usability of data. An example of standardized data format is NeXus for Neutron and X-ray experimental data.
A standardized data format include metadata schemas and formats. Metadata schemas facilitate data discovery and understanding. Examples of metadata schemas are DataCite Metadata Schema[1] and Dublin Core Metadata Element Set (DCMES)[2]. odML (open Metadata Markup Language)[3] is an example of a format for storing metadata suitable for both humans and machines.
[1]: https://schema.datacite.org
[2]: https://www.dublincore.org
[3]: https://www.incf.org/sbp/odml
9.Repository selection: Has your facility identified appropriate repositories for the storage and publication of research data?
Secure and accessible repositories are essential for the preservation and sharing of research data. Some criteria for repository selection are reputation and accessibility, data format support, metadata support, and access controls. Examples of suitable repositories are: Dryad[1], figshare[2], Open Science Framework (OSF)[3], and Zenodo[4].
[1]: https://datadryad.org/
[2]: https://figshare.com
[3]: https://osf.io
[4]: https://zenodo.org
10.Migrating legacy data: Are procedures in place to migrate legacy data to common standards over time?
This is essential for ensuring that legacy data remains accessible, understandable, and interoperable with modern data management systems and tools.

Questions cover default embargo periods, immediate open access, exception handling procedures, justifications for embargo periods, handling industry partnerships, periodic data sensitivity reviews, and processes for moving data to open access.
11.Default embargo period: Does your facility have a default embargo period post-data generation?
A default embargo period can protect data while planning for later open access. Here is the relevant excerpt from the proposal: “The default for published data sets of the project will be a time-embargoed access following the standard established at the project partner institutions. Project partners and facility users have the option to grant full access earlier then the embargo time, but can also ask for special exceptions for closed or restricted access in case of full/embargoed access conflicting with IP rights (e.g. patents). The repository mechanisms still enable granting individual access to embargoed/restricted data, e.g. for reviewers or project partners.”
12.Exception handling: Does your facility have a clear procedure for researchers to request exceptions to the default embargo period?
Procedures for handling exceptions ensure that researchers can balance the need for protection of intellectual property with the principles of Open Science.
13.Data sensitivity review: Does your facility have a process in place for periodically reviewing the sensitivity of data to evaluate if it can be moved to open access?
Ongoing reviews can identify opportunities for wider access as circumstances change.

This section highlights aspects related to the discoverability of ReMade@ARI datasets. Topics include features in the metadata catalog, EU catalogue registration, data citation mechanisms, tracking usage metrics, showcasing examples of data reuse, and promoting the impact of open research data.
14.Discoverability features
15.EU catalogue registration
16.Usage metrics
17.Notable examples

Focusing on the long-term preservation of ReMade@ARI project data, this section explores preservation plans, cost assessments, data lifecycle management practices, risk assessments, and mitigation strategies for potential data loss.
18.Cost assessment: Does your facility have a clear assessment of the costs involved in the long-term preservation of research data?
19.Risk assessment: Has your facility conducted a risk assessment pertaining to long-term data loss, and do you have mitigation strategies in place?

Addressing the alignment with OpenAIRE[1] guidelines [2] (see also their terms of agreements for content providers[3], how to comply with Horizon Europe mandate for research data management[4] and for publications[5]), this section assesses adherence to guidelines, repository registration with OpenAIRE, metadata harmonization, and compliance with OpenAIRE’s data access level requirements for ReMade@ARI research outputs.
20.Guideline adherence: Does your facility follow the OpenAIRE guidelines for literature repository managers when archiving research outputs?
21.Project repository registration: Is your facility’s repository for research outputs registered with OpenAIRE?

Current weak points, challenges faced, training needs assessment, and feedback mechanisms within the ReMade@ARI project. These aspects provide insights into areas that require attention and improvement.
22.Current weak points: Has your facility identified any gaps in achieving full adherence to the Open Science principles? If so, what are they?
Identifying gaps is the first step towards addressing them. Common gaps in adherence to open science principles are in data access and sharing (limited access to data, lack of guidelines for data citation), reproducibility (inadequate documentation, limited meta data), and collaboration (lack of incentives for data sharing).
23.Open science challenges and threats: What are the biggest challenges and threats your facility faces in implementing Open Science practices?
Understanding these can help to target support where it is most needed.
24.Open science opportunities: What new opportunities can your facility identify to further advance Open Science practices?
Seeking opportunities allows for innovation and growth in Open Science adoption.
25.Feedback mechanisms: What mechanisms are in place at your facility for researchers to provide feedback on Open Science practices?
Feedback mechanisms enable continuous improvement.