LAUDS Factories Data management plan

Lauds Factories - Co-funded by the European Union, 2024-2026 GA 101135986 Authors Julien Colomb Robert Mies LAUDS FACTORIES D6.1 Data management Plan 28 July 2025 Ref. Ares(2025)6071128 - 28/07/2025 Review and approval status Name and Surname Role in the Project Partner Author(s) Julien Colomb, Robert Mies Project manager TUB Reviewed by Matteo Subset Project member SUPSI Adam Burns Project member DYNE Approved by Robert Mies Project coordinator TUB History of changes Version Date Description of changes By 1.0 30.05.2024 First release Julien Colomb 2.0 30.06.2025 Adding formatted content + added summary, update in DMP Julien Colomb 2.1 20.07.2025 Added information on webcontent, correct SUPSI department in Argos, update index and formatting, adding figure 1.. Julien Colomb 2.2 27.07.2025 Integration of Argos content in the text file, headers formating and new table of content Julien Colomb Details Dissemination level Open Access Due date 30.05.2024 (v1) Issue date 28.07.2025 Contract No. 869984 Responsible Partner TUB Keywords Open source hardware, open design, company-community collaboration, etc. Summary This data management plan has been created following the organization of the consortium work. It was created at M6 after discussion with task leaders and updated at M18. While personal data is managed by the corresponding data collector according to GDPR rules, minimized datasets is shared with the whole consortium. In order to facilitate collaboration, non-sensitive research data is completely managed by the TUB partner, and shared via the TUB cloud infrastructure (a Nextcloud instance). A pioneering approach using Git version control is then used by the TUB partner to curate the datasets. It also provides a third backup of the data, following data security best practices (3 copies on 3 locations and at least 2 hardware types: local copy, Tub-cloud copy, Gitlab copy). This DMP presents first a summary organized following datasets types, while a detailed description is provided following the H2020 DMP template. We indicate how data will be collected, stored and made public or archived to maximize the transparency of the results. Following open science practice, this DMP is also made public, in order to facilitate its reuse and its implementation. 1. Introduction and scope.............................................5 2. Data flow...................................................................5 3. Summary information.............................................6 1 Literature review (WP1, WP2, WP3).....................................................6 2 Open Educational resources (WP3)....................................................6 3 Community feedback...........................................................................6 4 Data provided by the Open Call projects...........................................7 5 Outreach data........................................................................................7 6 Summary table......................................................................................7 4. DMP export from Argos............................................9 1 Main Info..................................................................................................9 2 Funding..................................................................................................10 3 License...................................................................................................10 4 Templates.............................................................................................10 Descriptions.............................................................................................10 Data collected during workshops.............................................................................................10 Data created to prepare workshop and interview..................................................................18 Education resources.................................................................................................................26 Survey data and analysis reporting........................................................................................34 Observational study.................................................................................................................40 Literature review data..............................................................................................................46 Interviews data and analysis reporting..................................................................................52 Co-pilots (open call) provided data.........................................................................................58 LAUDS-technical data.............................................................................................................65 Table of Figures Figure 1: Data flow flip-chart. While personal data stays in the responsibility of each data collector, non-personal data is shared with the whole consortium on the TUB cloud. The TUB team is responsible for its management and does publish open data on Zenodo and will archive unpublished data at the end of the project.....................................................................................5 1. Introduction and scope This documents takes for granted the reader is familiar with the objectives and purpose of a data management plan, as well as regulations about the collection and use of personal data. As explained in the M6 report, the work who lead to the redaction of this document was instrumental in the organization of the collaborative work, and lead to common decisions on the tools to be used for data collection and management. This document restrict itself to the presentation of the results of these discussions. The primary audience of this document are the LAUDS Factories data managers and data collectors, who can refer to this document for the implementation of the plan. This document was also made public, in the hope to facilitate its reuse by other researchers, and to raise the awareness of the LAUDS Factories partners about the importance of the implementation of this plan. 2. Data flow Data is collected by the different institutions and managed centrally by the TUB team, with the exception of personal data. If the data does not contain personal information, it is saved on the shared space of the TUB-cloud, such that it is automatically shared with all members of the LAUDS Factories project. If the raw data contains personal data, it is kept by the data collector in their institution or saved in a private space of the TUB-cloud that can be accessed only by the data collector and the TUB team. Minimized and derived data (which do not contain sensitive information) is then saved on the shared space of the TUB-cloud. The data is curated and version controlled by the TUB team, who is responsible to detect early issues with the data flow. Data supporting the deliverables is published on Zenodo, while the other files will be archived at the TUB institution (and erased from the TUB-cloud) six months after the end of the project. Figure 1: Data flow flip-chart. While personal data stays in the responsibility of each data collector, non-personal data is shared with the whole consortium on the TUB cloud. The TUB team is responsible for its management and does publish open data on Zenodo and will archive unpublished data at the end of the project. 3. Summary information We got three main source of research data: literature review, open education resources and data obtained from questioning the communities via surveys, interviews and workshop. In addition, as determined during the M18 report, we also plan to perform short interviews for outreach purposes, collect different data linked to the open call projects, and collect (usage) data in the LAUDS Factories. This summary provides a first entry point covering the most important factors of data management for the LAUDS Factories members, while the annex is giving more details for the actual management implemented by the TUB team. 1 Literature review (WP1, WP2, WP3) The literature review was organized using Zotero, which allows to write notes directly in the literature papers in their pdf form. Notes and pdf were made public using an openly accessible Zotero group, and collaborative working could happen there: https://www.zotero.org/groups/5445340/lauds_factories . T he Zotero data is also backed up on the TUB-cloud, in a specific folder shared by the whole consortium. 2 Open Educational resources (WP3) Training material has been created and handbooks were collected. Additional work is planned to collect educational resource data created and published by different projects. The workflow to create FAIR and open data out of this collected data will be defined in WP3. 3 Community feedback Different work package are collecting ideas and feedback from the different actors of the LAUDS Factories ecosystem. This can take the form of surveys, interviews or workshops, and it involves the collection of personal data (names, address, email addresses, voice, images). In this case, the raw data is preserved at the data collector institution, or using a private TUB-cloud space. Minimized data (minimized survey data, interview transcripts, or workshop reports) is shared with the consortium on the TUB cloud in the form of spreadsheets (.CSV files) or text documents (.docx, .pdf files). Derived data and data analysis code is saved on the Tub-cloud, and final versions are shared as open data and open code on Zenodo, under a CC-BY license. 4 Data provided by the Open Call projects Inside the WP4, Inova+ is collecting specific data from the open call participants: 3 presentations are recorded and used to create reports (docx files) and the project blueprint (pdf file), OC participants are also uploading documents: the final report, photo and text presentations, and the data uploaded during the application. Technical data on machine usage is also recorded by the LAUDS factories. The data is managed as follows: - Presentations are described in the “Interviews data and analysis reporting” plan - Uploaded documents are described in the “Co-pilots (open call) provided data” plan. - Technical data is described in the “LAUDS-technical data” plan. 5 Outreach data Webinar videos are provided on the website and on social media. As data protection on social media is impossible, we ask the interviewee to publish their interview as co-authors under a permissive license. This is then not considered collected data anymore, but is a published data we will reuse. Content provided by the LAUDS members for the website is also not treated as data, as the LAUDS members are authoring the web publication, and SUPSI is only having an editorial role. 6 Summary table Below, you will find a table summarizing the kind of data per task. Table 1: Table 1: Overview of type of data per task, OC related data is not included in this table. Task 1.1 1.2 1.3 1.4 1.5 2.1 2.2 2.3 2.4 2.5 3.1 3.2 3.3 3.4 4.1 4.2 4.3 4.4 5.1 5.2 5.3 5.4 5.5 workshop 1 1 1 1 1 1 1 interview 1 1 1 1 1 survey 1 1 1 1 1 literature_review 1 1 1 1 1 1 1 1 1 1 1 interfacer data 1 1 1 1 Co-pilot reports 1 1 1 call data 1 1 1 1 1 education resources observational study 4. DMP export from Argos This section is an export of the detailed DMP created using the Argos tool, with the Template “horizon Europe v5.0”. Its formatting was modified slightly to fit this document, but the content may be difficult to grasp for non-specialists, as some context is lost during the export.. 1 Main Info Title of DMP: Lauds-factories DMP Description: LAUDS factories is an innovative concept aiming to create small, versatile factories in local and urban areas to co-create and produce customized products in small series. It seeks to incorporate innovative and active resiliency capabilities at production and supply chain levels to support a green, circular, and digital transformation. It can enable artists, creatives, and entrepreneurs to test new ideas and products, and reduce the carbon footprint by cutting transportation costs and time. It also aims to create a more personalized experience for customers, enhance their satisfaction, loyalty, and dynamize the job market. The key exploitable results include a sustainable model for local manufacturing, an updated digital product passport for transparency, and innovation services for maker-spaces SMEs and creatives. The project has five work packages (WP) on top of the project management one. This DMP is organized via type of experiment done (literature review, interview, survey, workshop,...) going across WPs. This first version of the DMP (v1.0) was created after one on one meeting with the different task leaders. The second version (v2.0) was worked on M18 and included more information on personal data management and its minimization, and added some data categories related to data collected during the open call projects. This second version went through a round of internal reviews, and its format was modified to be similar to other deliverables of the project. Researchers: Robert Mies (orcid:0000-0003-0195-7405) Julien Colomb (orcid:0000-0002-3127-5520) Organizations: FAB CITY HAMBURG EV HELMUT SCHMIDT UNIVERSITAT UNIVERSITAT DER BUNDESWEHR HAMBURG BAUHAUS-UNIVERSITAET WEIMAR HIWW HAMBURGER INSTITUT FUR WERTSCHWERTSCHOPFUNGSSYSTEMATIK UND WISSENSMANAGEMENT UG (HAFTUNGSBESCHRANKT) 3.4.4 Do you intend to ensure (re)use by third parties after your project finishes? No 3.4.5 Is provenance well documented? Yes 3.4.6 What documented procedures for quality assurance do you have in place? Other Internal review 4 Allocation of Resources 4.1 Allocation of resources 4.1.1 What will be the cost of making the described output FAIR? 20 Euro • Storage • Archiving 4.1.2 How will this cost be covered? Use of institution infrastructure 4.1.3 Identify the people who will be responsible and their role(s) in the management of the described output Julien Colomb (orcid: 0000-0002-3127-5520) Dr. Colomb will curate the data once shared on the TU-cloud. Raw data will be organized by each data collector. One data collector will be named for each task. 5 Security 5.1 Data Security 5.1.1 What security measures are followed? • Firewall • Passwords 5.1.2 What conditions do the security measures meet? • Data access • Data storage • Data sharing 6 Ethical Aspects 6.1 Ethical aspects 6.1.1 Are there any ethical or legal issues that can have an impact on sharing the described dataset / output? yes 6.1.2 Does the described dataset / output contain sensitive information? Yes 6.1.3 Does the described dataset / output contain personal data? Yes 6.1.4 What are the methods used for processing and accessing sensitive/personal information? • Anonymizing data where necessary • Data accompanied by informed consent statements Yes 7 Other Issues 7.1 Other 7.1.1 Do you make use of other procedures for data management? Yes 7.1.2 Documentation of other procedures Shared data (meaning pseudo anonymized data) is version controlled using the Git technology by Dr. Colomb Data created to prepare workshop and interview Different files will be create to prepare data collection and analysis. This data does not contain any personal data and might be interesting for researchers planning similar experiments. This describes: - a code to be used in the analysis of WP1 and WP3 data - boards prepared for worshops - report templates for worshops - list of questions for interviews - list of questions and allowed answers for surveys. This correspond to tasks: workshops: 1.2, 1.4, 1.5, 2.1 Interviews: 1.2, 1.3, 5.3 Surveys 1.3, 2.2,5.2 Template: Horizon Europe Type: Dataset 1 Summary 1.1 Brief description of the described research output 1.1.1 What kind of research output are you describing? Models 1.1.2 Is it physical or digital? Digital 1.1.3 Are you generating or re-using it? New This topic does not have a code, interview questions or surveys to be reused to our knowledge. 1.1.4 What is the type of the described dataset? Reference or canonical 1.1.5 What is its format? Spreadsheet in .csv form or text. For surveys, we may have additional data format. 1.1.6 What is its expected size? 200 kb 1.1.7 Why are you collecting/generating or re-using it? • To obtain information • To combine with other data The code will be used as a base for qualitative text analysis. The other data will be used to collect data efficiently. 1.1.8 What is its origin / provenance? created internally 1.1.9 To whom might it be useful ('data utility')? • Researchers • Industry 2 Links Between Outputs 2.1 Publications 2.1.1 Does the described output support any scientific publication? No 2.1.2 Is there a data availability statement provided along with the publication? No 2.2 Datasets 2.2.1 Does the described output use or support any published dataset? No 2.3 Software 2.3.1 Does the described output use or support any software? Yes https://ec.europa.eu/eusurvey/ 3 FAIR Practices 3.1 Making data and other outputs findable, including provisions for metadata 3.1.1 Making data findable, including provisions for metadata 3.1.1.1 What type(s) of persistent identifier(s) are used for the described dataset / output? • Data identifiers • Researchers identifiers • Organizations identifiers • Projects identifiers DOI ORCID ROR Cordis 3.1.1.2 Will you provide metadata for the described dataset / output? Yes Dublin Core 3.1.1.3 What type(s) of metadata? • Descriptive • Reference • Legal 3.1.1.4 Do the metadata use standardized vocabularies? No 3.1.1.6 Are the metadata searchable? Yes 3.1.1.7 How are searchable metadata provided? Registry/Catalogue 3.1.1.8 Are keywords provided in the metadata? Yes 3.1.1.9 Are metadata harvestable? Yes 3.2 Making data and other outputs openly accessible 3.2.1 Repository 3.2.1.1 In which repository will the dataset / output be deposited? Zenodo 3.2.1.2 Is the selected repository a trusted source? Yes • Has certification • Follows repository standards • Details terms of use • Has an open access content policy • Provides Open Access content (free at the point of use) • Assigns PIDs • Follows metadata standards • Uses non-proprietary formats • Supports authentication and authorization of users • Has data security mechanisms in place 3.2.1.5 Does the repository(ies) assign datasets / outputs with persistent identifiers? Yes 3.2.1.7 Does the repository support versioning? Yes 3.2.2 Data 3.2.2.1 What is the described dataset / output title? TBD 3.2.2.2 How is the dataset / output shared? Open 3.2.2.5 Are there any methods or tools required to access the dataset / output? No 3.2.2.8 Is the described dataset / output supported by a data access committee? No 3.2.2.10 Please specify how long after the project has ended the dataset / output will be made accessible for At least 10 years, probably indefinitively 3.2.3 Metadata 3.2.3.1 Will you provide metadata even if the described dataset / output can not be openly shared? Yes 3.2.3.2 Under which license will metadata be provided? Creative Commons Zero (CC0) 3.2.3.3 Do metadata provide information about how to access the described dataset / output? No 3.2.3.4 Will metadata remain available after the dataset / output is no longer available? Yes 3.3 Making data and other outputs interoperable 3.3.1 Does your (meta)data use a controlled vocabulary? No 3.3.3 Have you applied a standard schema for your (meta)data? No 3.3.4 Will you provide a mapping to more commonly used ontologies? No 3.3.7 Does the described dataset / output provide qualified references with other outputs? No 3.4 Increasing data and other outputs reuse 3.4.1 What internationally recognized license will you use for your dataset / output? Creative Commons Attribution 4.0 3.4.2 What reusability and / or reproducibility methods are followed? • Readme files • Variable definitions 3.4.4 Do you intend to ensure (re)use by third parties after your project finishes? Yes 3.4.5 Is provenance well documented? Yes 3.4.6 What documented procedures for quality assurance do you have in place? Other Internal review 4 Allocation of Resources 4.1 Allocation of resources 4.1.1 What will be the cost of making the described output FAIR? 0 Euro • Storage • Archiving • Re-use 4.1.2 How will this cost be covered? • Use of national infrastructure • Use of institution infrastructure 4.1.3 Identify the people who will be responsible and their role(s) in the management of the described output Julien Colomb (orcid: 0000-0002-3127-5520) Dr. Colomb will curate and publish the data. 5 Security 5.1 Data Security 5.1.3 How will you preserve the described dataset / output in the long term? On top of the repository (Zenodo) we will set an archive of that data for at least 10 years. 6 Ethical Aspects 6.1 Ethical aspects 6.1.1 Are there any ethical or legal issues that can have an impact on sharing the described dataset / output? no 6.1.2 Does the described dataset / output contain sensitive information? No 6.1.3 Does the described dataset / output contain personal data? No 7 Other Issues 7.1 Other 7.1.1 Do you make use of other procedures for data management? No 4 Allocation of Resources 4.1 Allocation of resources 4.1.1 What will be the cost of making the described output FAIR? 0 Euro • Storage • Archiving • Re-use 4.1.2 How will this cost be covered? • Use of national infrastructure • Use of institution infrastructure 4.1.3 Identify the people who will be responsible and their role(s) in the management of the described output Julien Colomb (orcid: 0000-0002-3127-5520) Dr. Colomb will curate and publish the data. 5 Security 5.1 Data Security 5.1.3 How will you preserve the described dataset / output in the long term? On top of the repository (Zenodo) we will set an archive of that data for at least 10 years. 6 Ethical Aspects 6.1 Ethical aspects 6.1.1 Are there any ethical or legal issues that can have an impact on sharing the described dataset / output? no 6.1.2 Does the described dataset / output contain sensitive information? No 6.1.3 Does the described dataset / output contain personal data? No 7 Other Issues 7.1 Other 7.1.1 Do you make use of other procedures for data management? No Survey data and analysis reporting Survey data consist of text in a spreadsheet form, exported from a survey tool. The text may then be analyzed using qualitative methods using the MaxQDa software or other techniques. As for workshop data, this data may contains personal data: it will not be published, and pseudo-anonimized data will be shared with the team. Task making interviews: 1.3, 2.2, 3.4, 4.4, 5.2 Template: Horizon Europe Type: Dataset 1 Summary 1.1 Brief description of the described research output 1.1.1 What kind of research output are you describing? Research Data 1.1.2 Is it physical or digital? Digital 1.1.3 Are you generating or re-using it? New 1.1.4 What is the type of the described dataset? Observational 1.1.5 What is its format? Survey responses are organized in spreadsheet we will provide in a .cs or .tsv format. 1.1.6 What is its expected size? 50 MB 1.1.7 Why are you collecting/generating or re-using it? To obtain information (see description) 1.1.8 What is its origin / provenance? Data is collected during surveys. Most surveys will be directed toward a very specific group of people, making anonymization impossible. 1.1.9 To whom might it be useful ('data utility')? • Research communities • Decision makers 2 Links Between Outputs 2.1 Publications 2.1.1 Does the described output support any scientific publication? No 2.1.2 Is there a data availability statement provided along with the publication? No 2.3 Software 2.3.1 Does the described output use or support any software? No 3 FAIR Practices 3.1 Making data and other outputs findable, including provisions for metadata 3.1.1 Making data findable, including provisions for metadata 3.1.1.1 What type(s) of persistent identifier(s) are used for the described dataset / output? None No PID will be used, the data will be archived internally. 3.1.1.2 Will you provide metadata for the described dataset / output? Yes Dublin Core 3.1.1.3 What type(s) of metadata? • Descriptive • Legal 3.1.1.4 Do the metadata use standardized vocabularies? No 3.1.1.6 Are the metadata searchable? No 3.2 Making data and other outputs openly accessible 3.2.2 Data 3.2.2.1 What is the described dataset / output title? Surveys answers 3.2.2.2 How is the dataset / output shared? Closed We will share the data internally with the team. The data will be archived at the TU Berlin but not shared outside as - it will have personal data - it is not very useful outside of our own analysis. We think sharing the result of the analysis will be sufficient. 3.2.2.3 What is the reason of limiting access to the dataset / output? Personal data presence 3.2.2.5 Are there any methods or tools required to access the dataset / output? No 3.2.2.8 Is the described dataset / output supported by a data access committee? No 3.2.2.9 Please specify how the dataset / output will be accessed during and after the project ends - raw data will be kept by the data collectors - derived, anonymized data will be shared with the team on the TUcloud - data will then be archived after the end of the project in the data collecting institutions or in the TU Berlin. password and 2 factor authentification. 3.2.2.10 Please specify how long after the project has ended the dataset / output will be made accessible for 10 years 3.2.3 Metadata 3.2.3.1 Will you provide metadata even if the described dataset / output can not be openly shared? Yes 3.2.3.2 Under which license will metadata be provided? Creative Commons Zero (CC0) 3.2.3.3 Do metadata provide information about how to access the described dataset / output? No 3.2.3.4 Will metadata remain available after the dataset / output is no longer available? Yes 3.3 Making data and other outputs interoperable 3.3.1 Does your (meta)data use a controlled vocabulary? No 3.3.6 What community-endorsed interoperability best practices are followed? the code used for analysis will be made openly accessible. 3.4 Increasing data and other outputs reuse 3.4.3 Will you provide the described dataset / output in the public domain? No 3.4.4 Do you intend to ensure (re)use by third parties after your project finishes? No 3.4.5 Is provenance well documented? Yes 3.4.6 What documented procedures for quality assurance do you have in place? Other Internal reviews 4 Allocation of Resources 4.1 Allocation of resources 4.1.1 What will be the cost of making the described output FAIR? 150 Euro • Storage • Archiving Direct cost 4.1.2 How will this cost be covered? Use of institution infrastructure 4.1.3 Identify the people who will be responsible and their role(s) in the management of the described output Julien Colomb (orcid: 0000-0002-3127-5520) 5 Security 5.1 Data Security 5.1.1 What security measures are followed? • Firewall • Passwords 5.1.2 What conditions do the security measures meet? • Data access • Data sharing 5.1.3 How will you preserve the described dataset / output in the long term? Institutions have their own preservation of the raw data. Derived data will be preserved at the TU Berlin. 6 Ethical Aspects 6.1 Ethical aspects 6.1.1 Are there any ethical or legal issues that can have an impact on sharing the described dataset / output? yes 6.1.2 Does the described dataset / output contain sensitive information? Yes 6.1.4 What are the methods used for processing and accessing sensitive/personal information? Data accompanied by informed consent statements No 7 Other Issues 7.1 Other 7.1.1 Do you make use of other procedures for data management? No Observational study In task 2.2, researchers will get into LAUDS Factories and both ask specific questions and collect information about the equipment available. Template: Horizon Europe Type: Dataset 1 Summary 1.1 Brief description of the described research output 1.1.1 What kind of research output are you describing? Research Data 1.1.2 Is it physical or digital? Digital 1.1.3 Are you generating or re-using it? New 1.1.4 What is the type of the described dataset? Observational 1.1.5 What is its format? We will collect data in the form of text and spreadsheets (.cs), photo and video data might also be collected. 1.1.6 What is its expected size? 2 GB 1.1.7 Why are you collecting/generating or re-using it? To obtain information (see description) 1.1.8 What is its origin / provenance? Data is collected during surveys. Most surveys will be directed toward a very specific group of people, making anonymization impossible. 1.1.9 To whom might it be useful ('data utility')? Research communities 2 Links Between Outputs 2.1 Publications 2.1.1 Does the described output support any scientific publication? No 2.1.2 Is there a data availability statement provided along with the publication? No 2.3 Software 2.3.1 Does the described output use or support any software? No 3 FAIR Practices 3.1 Making data and other outputs findable, including provisions for metadata 3.1.1 Making data findable, including provisions for metadata 3.1.1.1 What type(s) of persistent identifier(s) are used for the described dataset / output? None No PID will be used, the data will be archived internally. 3.1.1.2 Will you provide metadata for the described dataset / output? Yes Dublin Core 3.1.1.3 What type(s) of metadata? • Descriptive • Legal 3.1.1.4 Do the metadata use standardized vocabularies? No 3 FAIR Practices 3.1 Making data and other outputs findable, including provisions for metadata 3.1.1 Making data findable, including provisions for metadata 3.1.1.1 What type(s) of persistent identifier(s) are used for the described dataset / output? None URL 3.1.1.2 Will you provide metadata for the described dataset / output? No 3.2 Making data and other outputs openly accessible 3.2.1 Repository 3.2.1.2 Is the selected repository a trusted source? No 3.2.1.5 Does the repository(ies) assign datasets / outputs with persistent identifiers? No 3.2.1.7 Does the repository support versioning? Unknown 3.2.2 Data 3.2.2.1 What is the described dataset / output title? LAUDS Factories Zotero library 3.2.2.2 How is the dataset / output shared? Open Open per default as soon as created 3.2.2.5 Are there any methods or tools required to access the dataset / output? No 3.2.2.8 Is the described dataset / output supported by a data access committee? No 3.2.2.9 Please specify how the dataset / output will be accessed during and after the project ends Via Zotero https://www.zotero.org/groups/5445340/lauds_factories. Backup and archive on the TUBcloud. 3.2.2.10 Please specify how long after the project has ended the dataset / output will be made accessible for At least 10 years, probably indefinitively 3.2.3 Metadata 3.2.3.1 Will you provide metadata even if the described dataset / output can not be openly shared? Yes 3.2.3.2 Under which license will metadata be provided? Creative Commons Zero (CC0) 3.2.3.3 Do metadata provide information about how to access the described dataset / output? No 3.2.3.4 Will metadata remain available after the dataset / output is no longer available? Yes 3.3 Making data and other outputs interoperable 3.3.1 Does your (meta)data use a controlled vocabulary? No 3.3.3 Have you applied a standard schema for your (meta)data? No 3.3.4 Will you provide a mapping to more commonly used ontologies? No 3.3.7 Does the described dataset / output provide qualified references with other outputs? No 3.4 Increasing data and other outputs reuse 3.4.1 What internationally recognized license will you use for your dataset / output? CC0 1.0 3.4.4 Do you intend to ensure (re)use by third parties after your project finishes? Yes 3.4.5 Is provenance well documented? Yes Person who had the reference is indicated in the library. 3.4.6 What documented procedures for quality assurance do you have in place? Not available 4 Allocation of Resources 4.1 Allocation of resources 4.1.1 What will be the cost of making the described output FAIR? 60 Euro • Storage • Archiving • Re-use Direct cost Zotero library 2 GB for 3 years. 4.1.2 How will this cost be covered? • Collaboration with other Projects • Other Zotero library cost already covered via other projects in the TUB 4.1.3 Identify the people who will be responsible and their role(s) in the management of the described output Julien Colomb (orcid: 0000-0002-3127-5520) Dr. Colomb will backup the data on the TUBcloud. 5 Security 5.1 Data Security 5.1.3 How will you preserve the described dataset / output in the long term? We will set an archive of that data for at least 10 years. 6 Ethical Aspects 6.1 Ethical aspects 6.1.1 Are there any ethical or legal issues that can have an impact on sharing the described dataset / output? no 6.1.2 Does the described dataset / output contain sensitive information? No 6.1.3 Does the described dataset / output contain personal data? No 7 Other Issues 7.1 Other 7.1.1 Do you make use of other procedures for data management? No Interviews data and analysis reporting Interview data consist of the videos or audios files, and the transcription text. The text will then be analyzed using qualitative methods using the MaxQDa software or other techniques. As for workshop data, this data contains personal data: it will not be published, and pseudoanonimized data will be shared with the team. Task making interviews: 1.2, 1.3, 3.1, 5.3 NB: data coming from reports from the OC will be treated following the same procedure: video raw data not shared, derived/pseudoanonimized data in text or pdf form will be share with the consortium, fully anonimized data will be shared openly on zenodo. Template: Horizon Europe Type: Dataset 1 Summary 1.1 Brief description of the described research output 1.1.1 What kind of research output are you describing? Research Data 1.1.2 Is it physical or digital? Digital 1.1.3 Are you generating or re-using it? New 1.1.4 What is the type of the described dataset? Derived or compiled The raw data is made of videos of interviews, the transcription of the interview is derived data in a text form. 1.1.5 What is its format? Video files in mp4 format. Transcripts are in text files in .txt or .docx format. They will be imported in the MaxQDa software which creates text files organized in one folder per code (the code will be treated as a different dataset). 1.1.6 What is its expected size? 50 GB 1.1.7 Why are you collecting/generating or re-using it? To obtain information (see description) 1.1.8 What is its origin / provenance? Data is collected during interviews 1.1.9 To whom might it be useful ('data utility')? • Research communities • Decision makers We will use the data to create a common and community-lead definition of lauds factories (3.1), define readiness levels and requirements for LAUDS Factories (1.2/1.3), and get feedback on the digital infrastructure provided (5.2). 2 Links Between Outputs 2.1 Publications 2.1.1 Does the described output support any scientific publication? No 2.1.2 Is there a data availability statement provided along with the publication? No 2.3 Software 2.3.1 Does the described output use or support any software? No 3 FAIR Practices 3.1 Making data and other outputs findable, including provisions for metadata 3.1.1 Making data findable, including provisions for metadata 3.1.1.1 What type(s) of persistent identifier(s) are used for the described dataset / output? None No PID will be used, the data will be archived internally. 3.1.1.2 Will you provide metadata for the described dataset / output? Yes Dublin Core 3.1.1.3 What type(s) of metadata? • Descriptive • Legal 3.1.1.4 Do the metadata use standardized vocabularies? No 3.1.1.6 Are the metadata searchable? No 3.2 Making data and other outputs openly accessible 3.2.2 Data 3.2.2.1 What is the described dataset / output title? Interviews and transcripts 3.2.2.2 How is the dataset / output shared? Closed We will share the data internally with the team. The data will be archived at the TU Berlin but not shared outside as - it will have personal data - it is not very useful outside of our own analysis. We think sharing the result of the analysis will be sufficient. 3.2.2.3 What is the reason of limiting access to the dataset / output? Personal data presence 3.2.2.5 Are there any methods or tools required to access the dataset / output? No 3.2.2.8 Is the described dataset / output supported by a data access committee? No 3.2.2.9 Please specify how the dataset / output will be accessed during and after the project ends - raw data will be kept by the data collectors - derived, anonymized data will be shared with the team on the TUcloud - data will then be archived after the end of the project in the data collecting institutions or in the TU Berlin. password and 2 factor authentification. 3.2.2.10 Please specify how long after the project has ended the dataset / output will be made accessible for 10 years 3.2.3 Metadata 3.2.3.1 Will you provide metadata even if the described dataset / output can not be openly shared? Yes 3.2.3.2 Under which license will metadata be provided? Creative Commons Zero (CC0) 3.2.3.3 Do metadata provide information about how to access the described dataset / output? No 3.2.3.4 Will metadata remain available after the dataset / output is no longer available? Yes 3.3 Making data and other outputs interoperable 3.3.1 Does your (meta)data use a controlled vocabulary? No 3.3.6 What community-endorsed interoperability best practices are followed? the code used for analysis will be made openly accessible. 3.4 Increasing data and other outputs reuse 3.4.3 Will you provide the described dataset / output in the public domain? No 3.4.4 Do you intend to ensure (re)use by third parties after your project finishes? No 3.4.5 Is provenance well documented? Yes 3.4.6 What documented procedures for quality assurance do you have in place? Other Internal reviews 4 Allocation of Resources 4.1 Allocation of resources 4.1.1 What will be the cost of making the described output FAIR? 150 Euro • Storage • Archiving Direct cost 4.1.2 How will this cost be covered? Use of institution infrastructure 4.1.3 Identify the people who will be responsible and their role(s) in the management of the described output Julien Colomb (orcid: 0000-0002-3127-5520) 5 Security 5.1 Data Security 5.1.1 What security measures are followed? • Firewall • Passwords 5.1.2 What conditions do the security measures meet? • Data access • Data sharing 5.1.3 How will you preserve the described dataset / output in the long term? Institutions have their own preservation of the raw data. Derived data will be preserved at the TU Berlin. 6 Ethical Aspects 6.1 Ethical aspects 6.1.1 Are there any ethical or legal issues that can have an impact on sharing the described dataset / output? yes 6.1.2 Does the described dataset / output contain sensitive information? Yes 6.1.4 What are the methods used for processing and accessing sensitive/personal information? Data accompanied by informed consent statements No 7 Other Issues 7.1 Other 7.1.1 Do you make use of other procedures for data management? No 6 Ethical Aspects 6.1 Ethical aspects 6.1.1 Are there any ethical or legal issues that can have an impact on sharing the described dataset / output? yes 6.1.2 Does the described dataset / output contain sensitive information? Yes 6.1.4 What are the methods used for processing and accessing sensitive/personal information? Data accompanied by informed consent statements No 7 Other Issues 7.1 Other 7.1.1 Do you make use of other procedures for data management? No LAUDS-technical data During open calls and during the project, LAUDS factories will collect data on the usage of their installations.. This is mostly related to WP2 activities, while having an output used in WP4 and the digital passport of the created hardware. Template: Horizon Europe Type: Dataset 1 Summary 1.1 Brief description of the described research output 1.1.1 What kind of research output are you describing? Research Data 1.1.2 Is it physical or digital? Digital 1.1.3 Are you generating or re-using it? New 1.1.4 What is the type of the described dataset? Observational 1.1.5 What is its format? Interfacer data is collected in GraphQL and specific spreadsheets data will be exported from it. Other tools may be used and the format of the data will be updated here. 1.1.6 What is its expected size? 1 GB 1.1.7 Why are you collecting/generating or re-using it? • To obtain information • To keep on record • To make informed decisions • To improve a product Usage data is important to have an idea of the resources used in the fabrication process, and determine putative improvement in the workflow. 1.1.8 What is its origin / provenance? Data is collected automatically in LAUDS Factories and via the interfacer software. 1.1.9 To whom might it be useful ('data utility')? • Research communities • Industry Mostly for maker-spaces and fablabs and their users. 2 Links Between Outputs 2.1 Publications 2.1.1 Does the described output support any scientific publication? No 2.1.2 Is there a data availability statement provided along with the publication? No 2.3 Software 2.3.1 Does the described output use or support any software? No 3 FAIR Practices 3.1 Making data and other outputs findable, including provisions for metadata 3.1.1 Making data findable, including provisions for metadata 3.1.1.1 What type(s) of persistent identifier(s) are used for the described dataset / output? None No PID will be used, there is no plan to publish this data yet. 3.1.1.2 Will you provide metadata for the described dataset / output? Yes Dublin Core 3.1.1.3 What type(s) of metadata? • Descriptive • Legal 3.1.1.4 Do the metadata use standardized vocabularies? No 3.1.1.6 Are the metadata searchable? No 3.2 Making data and other outputs openly accessible 3.2.1 Repository 3.2.1.7 Does the repository support versioning? Unknown 3.2.2 Data 3.2.2.1 What is the described dataset / output title? technical usage data 3.2.2.2 How is the dataset / output shared? Closed We will share the data internally with the team. The data will be archived at the TU Berlin but not shared outside as it belongs to the LAUDS Factories. This may change at a later stage of the project. 3.2.2.3 What is the reason of limiting access to the dataset / output? dataset quality and consistency may be too low to make reuse possible. 3.2.2.5 Are there any methods or tools required to access the dataset / output? No 3.2.2.8 Is the described dataset / output supported by a data access committee? No 3.2.2.9 Please specify how the dataset / output will be accessed during and after the project ends - raw data will be kept by the data collectors - derived data will be shared with the team on the TUcloud - data will then be archived after the end of the project in the data collecting institutions or in the TU Berlin. password and 2 factor authentification. 3.2.2.10 Please specify how long after the project has ended the dataset / output will be made accessible for 10 years 3.2.3 Metadata 3.2.3.1 Will you provide metadata even if the described dataset / output can not be openly shared? Yes 3.2.3.2 Under which license will metadata be provided? Creative Commons Zero (CC0) 3.2.3.3 Do metadata provide information about how to access the described dataset / output? No 3.2.3.4 Will metadata remain available after the dataset / output is no longer available? Yes 3.3 Making data and other outputs interoperable 3.3.1 Does your (meta)data use a controlled vocabulary? No 3.3.6 What community-endorsed interoperability best practices are followed? the code used for analysis will be made openly accessible. 3.4 Increasing data and other outputs reuse 3.4.3 Will you provide the described dataset / output in the public domain? No 3.4.4 Do you intend to ensure (re)use by third parties after your project finishes? No 3.4.5 Is provenance well documented? Yes 3.4.6 What documented procedures for quality assurance do you have in place? Other Internal reviews 4 Allocation of Resources 4.1 Allocation of resources 4.1.1 What will be the cost of making the described output FAIR? 150 Euro • Storage • Archiving Direct cost 4.1.2 How will this cost be covered? Use of institution infrastructure 4.1.3 Identify the people who will be responsible and their role(s) in the management of the described output Julien Colomb (orcid: 0000-0002-3127-5520) 5 Security 5.1 Data Security 5.1.1 What security measures are followed? • Firewall • Passwords 5.1.2 What conditions do the security measures meet? • Data access • Data sharing 5.1.3 How will you preserve the described dataset / output in the long term? Institutions have their own preservation of the raw data. Derived data will be preserved at the TU Berlin. 6 Ethical Aspects 6.1 Ethical aspects 6.1.1 Are there any ethical or legal issues that can have an impact on sharing the described dataset / output? yes 6.1.2 Does the described dataset / output contain sensitive information? Yes 6.1.4 What are the methods used for processing and accessing sensitive/personal information? Not available no sensitive information 7 Other Issues 7.1 Other 7.1.1 Do you make use of other procedures for data management? No Powered by Technische Universität Berlin / Grenoble Inp Université Grenoble Alpes / Helmut Schmidt Universität Universität der Bundeswehr Hamburg / Université de Lorraine / Zentrum für Soziale Innovation Gmbh Inova+ Innovation Services, S.A / Maker / Stichting Dyne.Org / Bauhaus-Universität Weimar / Fab City Hamburg E.V.Hiww Ug / SUPSI University of Applied Sciences and Arts of Southern Switzerland LAUDS Local Accessible Urban Digital Sustainable Factories is a Horizon Europe research and innovation action - Co-funded by the European Union, 2024-2026, GA 101135986