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-====== Institutional Research Data Repository of HSB: GRO.data ======
-**Welcome** to the wiki World of Research Data Management (RDM), the world of Open Science and Open Education!
-Research data is a **shared resource** and a **common good** that is essential for excellent **research**, excellent **education**, and excellent **science**. Be prepared to share and publish your research data in accordance with **Good Research Practice** in order to gain **academic credit** and **academic recognition** for your research, and to contribute the **advancement of science** in your field of research for the benefit of society and **global unity**.
-The FDM@HSB team is here to support your research, providing RDM tools and services including the HSB Institutional Research Data Repository.
-This RDM Wiki provides background information on the HSB institutional research data repository, demonstrates how it can support your research. It also provides the information you need to **implement it for your research data**.
-We, the FDM@HSB team, greatly **appreciate** you taking the time to read through the wiki for your research and **sincerely thank you** for **your valuable contribution** and **commitment** to using the repository! We are here to help and welcome your **feedback**, as this plays a key role in improving our service.
-Should you have any questions or need further assistance, please do not hesitate to contact us at **fdm@hs-bremen.de**.
-Let’s work together to
-  * **archive**  and/or **share**  your research data,
-  * enhance its **discoverability**,
-  * facilitate its **reuse**  by others, and
-  * enable your **contribution**  to the ideal of **open science**.
-See also:
-  * [[https://www.hs-bremen.de/assets/hsb/de/Dokumente/Referate/R02/Rechtssammlung/Qualit%C3%A4tssicherung/Forschungsdaten-Policy_der_Hochschule_Bremen__Fassung_Amtliche_Mitteilungen_der_HSB_2-2025_vom_2025-02-03_.pdf|Forschungsdaten-Policy der Hochschule Bremen]], February 3, 2025, in German
-  * [[https://www.dfg.de/en/basics-topics/basics-and-principles-of-funding/good-scientific-practice|Good Research Practice]]
-    * [[https://zenodo.org/records/14281892|Guidelines for Safeguarding Good Research Practice, Code of Conduct]]
-    * [[https://www.leibniz-fli.de/research/good-research-practice|For Building Trust in Science: Good Research Practice (GRP)]], Leibniz Institute on Aging – Fritz Lipmann Institute
-  * FAIR principles
-    * [[https://www.go-fair.org/fair-principles/|GO FAIR]]
-    * [[https://force11.org/info/the-fair-data-principles/|FORCE11]]
-    * and its first publication [[https://www.nature.com/articles/sdata201618|Wilkinson et al. 2016]].
-<note tip>If you are confident with the topic of repositories and research data management, or prefer a practical approach, please refer to our one-page **Lightning Start Graphic**  and **Quick Start Guide**.</note>
-===== 1. Institutional research data repository of HSB: GRO.data =====
-===== What is GRO.data? =====
-  * **GRO.data**  is an institutional Research Data Repository. \\ It is managed by the [[https://www.eresearch.uni-goettingen.de/|eResearch Alliance]], a joint group of [[https://www.sub.uni-goettingen.de/en|SUB]], [[https://gwdg.de/en/|GWDG]], [[https://www.umg.eu/|UMG]] at the Campus in Göttingen.
-  * A **Repository**  in the digital world is a place, where digital information is stored, and can be found and retrieved.
-  * A **Research Data Repository**  in the context of Research Data Management (RDM) is a digital storage space that enables researchers and academics to store and archive their data, and make them more **discoverable**, **reusable **and **accessible**.
-  * An **institutional **Research Data Repository generally serves the researchers or academics of that institution or organisation.
-  * GRO.data is based on the open source software **Dataverse**.
-===== What is Dataverse? =====
-  * Dataverse is an open source web-based research data repository that is designed to help researchers and organisations to **manage**, **archive**, **publish**, **share**, and **preserve **their data.
-  * Dataverse was initially developed at Harvard University, and is being used on almost all continents since 2006. It has a **strong **and **vibrant community**, constantly improving the software.
-  * It provides a **robust **and user-friendly environment for data management, ensuring that valuable research data is well organised, accessible, **maintained** and preserved **over time**.
-  * Dataverse promotes **open science** by **facilitating data**, **sharing** and **collaboration** within the research community. It follows the **FAIR**  principles, making data **F**indable, **A**ccessible, **I**nteroperable and **R**eusable. This enables researchers to more **easily replicate** the work of others.
-See below for more details about the software Dataverse. \\ See also: [[https://dataverse.org/|Dataverse]], [[https://dataverse.harvard.edu|Harvard Dataverse Repository]]. \\
-To learn about the **initial idea**  of Dataverse, please read [[https://gking.harvard.edu/files/abs/dvn-abs.shtml|[King 2007]]].
-===== Why should I use a research data repository? =====
-A research data repository helps you
-  * **archive** and/or **publish** your research data.
-  * ensure your research data can be **found**, and **reused**  by others.
-  * follow **best practice**, and conform with **integrity**  and **transparency**, which is the core value of **Good Research Practice**.
-  * get the **academic credit** and academic **recognition** you deserve
-  * achieve **long-term trust**, recognition and **sustainable success**.
-  * contribute to the ideal of **open science**  and **open education**.
-See also:
-  * [[https://zenodo.org/records/6472827|Guidelines for Safeguarding Good Research Practice, Code of Conduct.]], DFG: Deutsche Forschungsgemeinschaft
-  * [[https://www.dfg.de/en/basics-topics/basics-and-principles-of-funding/good-scientific-practice|Good Research Practice]], DFG
-  * [[https://www.go-fair.org/fair-principles/|FAIR principles]], GO FAIR Initiative
-  * [[https://force11.org/info/the-fair-data-principles/|The FAIR Data Principles]], FORCE11: The Future of Research Communications and e-Scholarship
-  * [[https://www.leibniz-fli.de/research/good-research-practice|Good Research Practice, Leibniz Institute on Aging – Fritz Lipmann Institute]]
-<WRAP center round tip 60%> \\ In general, we recommend selecting a **subject-specific** repository that aligns with your intended use because of its discipline-specific community and the high relevance of a scientific field. \\ </WRAP>
-If you are looking for a subject-specific repository, [[https://www.re3data.org|re3data]] is a global registry of research data repositories that can help facilitate your search.
-**GRO.data**  is a **general-purpose**  research data repository adapted by the Göttingen eResearch Alliance. It supports you to
-  * **publish**,** archive, **and **share**  your research data.
-Your data can be enriched with **metadata**, and get **persistent identifiers**, PIDs, such as **DOIs**. These metadata are propagated to **the central DOI database**, which is used by different **search engines**  like [[https://www.datacc.org/en/your-needs/finding-data/browsing-scientific-search-engines-data-journals/|Datacite Search]].
-In addition, GRO.data offers features such as **versioning**, **data citation**, file previews, license assignment, file restriction, and **controlled access permissions**  via roles and responsibilities. Furthermore, the functionalities are highly **extensible**.
-<WRAP center round info 60%> \\ **HSB provides** GRO.data as an **institutional research data repository** in **cooperation** with the **GWDG**, which manages and operates the GRO.data.
-Its primary purpose is to support the storage and archiving of <nowiki>"</nowiki>**cold**<nowiki>"</nowiki> research data that is **infrequently** accessed, and to **make** it **available** to **others**. \\ </WRAP>
-===== What is the GWDG? =====
-  * The [[https://gwdg.de/en/about-us/|GWDG ]]is a joint institution of [[https://www.uni-goettingen.de/en/1.html|the University of Göttingen]] and [[https://www.mpg.de/en|the Max Planck Society]].
-  * It serves as an **IT competence centre**  and **data centre**  with over **50 years of experience**. It is one of the locations for a **supercomputer **operated by the North German Supercomputing Alliance, a collaboration of seven federal states in Northern Germany.
-  * The GWDG provides **future**  and **customer-oriented**  reliable **services **and **infrastructure **to support and advance science for its excellence in research and teaching, now and in the future. See also [[https://gwdg.de/en/about-us/mission-statement/|Mission Statement]].
-  * **Data ****processing**  has always been an important service of the GWDG, and along with the development of technologies, the GWDG continues to provide different solutions to support science and research. **Data archive**, **data management plans**, **data repository**, **publication management**, which support researchers, scientists, data scientists, data stewards with their research data, are some of the examples.
-===== Where and how is my data stored? And how secure is my data? =====
-  * GRO.data is hosted in academic data centre according to **German data protection** and **data security directives**. The authentication and authorisation are processed and operated via [[http://https://academiccloud.de/|the Academic Cloud]]. [[
-https://www.aai.dfn.de/index.en.html|DFN-AAI-Service]] is applied to carry out authentication and authorisation using the software “Shibboleth”. DFN-AAI is a service infrastructure for research and education communities in Germany.
-  * The Academic Cloud was developed to support scientific use and research in Lower Saxony, is provided by the **GWDG** in **Göttingen**, gets support in planning and operation by [[https://www.lanit-hrz.de/|LANIT]], and is co-funded by the [[https://www.mwk.niedersachsen.de/startseite/|MWK]] in Hannover. The Academic Cloud offers proven software applications as reliable cloud services. Uses can access the services with a single account through a uniform portal.
-  * The data is stored in **multiple locations** across Göttingen in order to ensure **redundancy**. There are **daily backups** of the data which are stored on disks and tapes, and the drives are **not on the same machine** with the application itself.
-  * The data storage process is one of **the most important assets** of the GWDG. It applies e.g., two factor authentication with dedicated hardware to access internal systems including the software application and the storage system with highly structured authentication and authorization management.
-See also: [[https://gwdg.de/en/about-us/catalog/terms-and-conditions/|Terms and conditions]], [[https://www.eresearch.uni-goettingen.de/imprint/|Imprint]], [[https://gwdg.de/en/privacy-notice/|Privacy Notice]] of the GRO.data website.
-==== What is a Cloud and Cloud Computing? ====
-  * Cloud and cloud computing is the concept of **on-demand use**  of **computing resources**, such as **physical **or **virtual servers**,** data storage**, **networking capabilities**, **software applications**, over the internet.
-  * This technology is more **eco-friendly**  than the traditional IT solutions, which reduces the consumption of energy and the ecological footprint, while simultaneously enhancing the **performance**, **scalability**, **availability**, **accessibility**, **flexibility **of IT services and its environment. See figure cloud computing.
-{{:fdmathsb:cloud_computing.png?700|}}
-===== Do you perhaps have a visual representation of GRO.data and the Academic Cloud? =====
-See:
-  * Figure: GRO.data and the Academic Cloud
-  * Figure: Conceptual View of GRO.data and the Academic Cloud
-{{:fdmathsb:gro.data_and_the_academic_cloud_2025-01-02.png?700}}
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-{{:fdmathsb:conceptional_view_of_gro.data_and_academic_cloud_with_caption.png?670}}
-----
-See also:
-  * [[https://dataverse.org/|Dataverse]]
-  * [[https://www.eresearch.uni-goettingen.de/|eResearch Alliance]]
-  *  [[https://gwdg.de/en/|GWDG]]
-      * [[https://www.youtube.com/watch?v=Hlc5E-PwVg8&list=PLvcoSsXFNRbk1fy3KcPrUk9VGUjBppp-b&index=2|50 Jahre GWDG – eine Zeitreise durch 50 Jahre wissenschaftliche Datenverarbeitung bei der GWDG]] (only in German)
-      * [[https://www.uni-goettingen.de/de/54088.html?id=6091|Northern Germany’s fastest computer]], Göttingen’s supercomputer “Emmy” fifth fastest in Germany and, 47th in the world
-  * [[https://www.sub.uni-goettingen.de/en|SUB]], Göttingen State and University Library
-  * [[https://www.mpg.de/de|The Max Planck Society]]
-  * FAIR principles:
-      * [[https://www.go-fair.org/fair-principles/|GO FAIR]], GO FAIR Initiative
-      * [[https://force11.org/info/the-fair-data-principles/|FORCE11]], The Future of Research Communications and e-Scholarship
-----
-===== 2. Getting Started =====
-We show you here the **key concepts**  and **main features** of GRO.data/Dataverse in more details **at your convenience** to understand the software better, and highlight all the **important aspects** of ** preparing **and **handling**  **data before**  you storing, archiving and publishing your data, which would be **keys** for processing your data into the GRO.data repository **smoothly**  and **efficiently**.
-We strive to prepare everything we can to **save your work** and **time**. However, if you have any further questions, please feel free to contact us at: **fdm@hs-bremen.de**.
-Let's **work together**  walk through the preparation process
-  * for publishing and/or archiving **your**  intended research **data** to achieve **your research goals**,
-  * for **others**  who will **reuse**  your data,
-  * for a meaningful **global**  world of research and science.
-----
-==== Dataverse Basic and main Features ====
-=== 1. Data Organisation and Management in the software Dateverse ===
-<nowiki>"</nowiki>**Dataverse collection<nowiki>"</nowiki>** and **<nowiki>"</nowiki>Dataset<nowiki>"</nowiki>** are **two basic concepts** of data organisation in the software Dataverse.
-The word “**dataverse**” has a **doubled meaning** in this context. To make it easier to understand and avoid any confusion, we use the notation “dataverse” for a “dataverse collection”, and the notation “Dataverse” for the software Dataverse, i.e., “**dataverse**” ⇒ “**dataverse collection**”, and “**Dataverse**” ⇒ **the software Dataverse**.
-A **dataverse** is a **container** for all your datasets, and other dataverses. They are virtual archives where you can **organise** your data. It can be setup for individual researchers, departments, organisations, etc. It helps you **manage** your data, and works like a **folder**.
-Each **dataverse** contains datasets and/or other dataverses, and each **dataset** contains **descriptive metadata** and **data files**, including a description of the methods, documentation and codes associated with the data. All of this will make it easier for other researchers to discovery and understand your dataset. i.e., **dataset** ⇒ **metadata** & **data** & **code** & **documentation**. And for the purpose of organising your research work, you can also **nest** dataverses into other dataverses, if you wish.
-All datasets and files are **automatically assigned a DOI** in the repository of GRO.data, when they are **published**. You can **control the access** to the data, for example you can open your data to the general public, or restrict access to it. Permissions and access control can also be applied for a single file.
-{{:fdmathsb:schematic_diagram_of_a_dataset_in_dataverse_4.0.png?direct&500}}
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-{{:fdmathsb:05_dataverse_2025-10-09.png?direct&500}}
-See also:
-  * [[https://guides.dataverse.org/en/latest/user/dataset-management.html|Dataset and File Management]]
-  * [[https://guides.dataverse.org/en/latest/user/dataverse-management.html|Dataverse Collection Management]]
-  * [[https://guides.dataverse.org/en/latest/user/dataverse-management.html#id6|Create a New Dataverse]]
-=== 2. Metadata Support ===
-=== 2.1 Supported Metadata ===
-A dataset has three types of metadata schemata
-  * **Citation Metadata**
-This is required and default setting. It is standardised citation of datasets, making easier for researchers to publish their data and get credit as well as recognition for their work. It is the metadata that are needed for generating a data citation.
-  * **Domain Specific Metadata** \\ Dataverse has currently special support for **Social Science**, **Life Science**, **Geospatial**, **Journal**, **Astronomy**  and **Astrophysics****datasets **:
-      * Geospatial Metadata
-      * Social Science & Humanities Metadata
-      * Life Sciences Metadata
-      * Journal Metadata
-      * Astronomy and Astrophysics Metadata
-  * **File-level Metadata**
-This varies depending on the type of data file. Examples include: editing file name as needed; adding file descriptions (file level “terms”); adding tags at the file level. See also [[https://guides.dataverse.org/en/latest/user/dataset-management.html#edit-file-metadata|Edit File Metadata]]
-Even more, you can have **customised metadata**, if you wish.
-Before creating custom metadata, consider how to best utilise  existing metadata, and carefully evaluate the necessity and usefulness of the custom metadata. While creating custom metadata offers advantages such as unique complementary information, it is also time-consuming. For a unique, large-scale and long-term project that produces significant  amounts of unique data, using custom metadata can be beneficial. One example is the Collaborative Research Centre 990, CRC990. See [[https://data.goettingen-research-online.de/dataverse/crc990|CRC990]].
-=== 2.2 Supported Metadata Export Formats ===
-Once a dataset has been published, its metadata can be exported in a variety of other metadata standards and formats, which help **make datasets more discoverable**  and **usable in other systems**, such as other data repositories. The following metadata export formats are available:
-  * Dublin Core
-  * DDI (Data Documentation Initiative Codebook 2.5)
-  * DDI HTML Codebook (A more human-readable, HTML version of the DDI Codebook 2.5 metadata export)
-  * DataCite 4
-  * JSON (native Dataverse Software format)
-  * OAI_ORE
-  * OpenAIRE
-  * Schema.org JSON-LD
-See also:
-  * [[https://dataverse.org/best-practices/data-citation|Data Citation Standard]]
-  * [[https://guides.dataverse.org/en/latest/user/appendix.html#metadata-references|Metadata References]]
-  * [[https://guides.dataverse.org/en/latest/user/dataset-management.html#supported-metadata-export-formats|Supported Metadata Export Formats]]
-  * [[https://guides.dataverse.org/en/latest/admin/metadatacustomization.html#|Metadata Customization]]
-=== 3. Licenses, custom licenses and Custom terms of use ===
-<note tip> The **DEAL**  consortium **recommends**  “**CC BY**” licence, **Open Access Means CC BY**: [[:https:deal_konsortium.de_en_why_ccby|https: //deal − konsortium.de/en/why – ccby]] \\
-However, you can select any licence, which fits your data and research or define your own terms of use for your data. </note>
-Custom licenses and custom terms of use allow users to define specific conditions for the access and usage of their data. While **the default setting is CC0 1.0**, which effectively waives all rights and allows unrestricted use of the data, users can choose to apply more tailored licenses. These custom licenses can specify restrictions on commercial use, attribution requirements, or modifications.
-Custom terms of use provide additional flexibility by outlining specific conditions related to data access, sharing, and redistribution. This ensures that data creators can maintain control over how their work is used while still making it available to others.
-See also:
-  * [[https://creativecommons.org/share-your-work/cclicenses/|Creative Commons (CC)]]
-  * [[https://pose.open.ubc.ca/open-education/creative-commons/what-is-creative-commons-and-what-are-creative-commons-licenses/|What is Creative Commons? ]]
-  * [[https://pose.open.ubc.ca/open-education/creative-commons/what-are-the-different-types-of-creative-commons-licenses/|What are the Different Types of Creative Commons Licenses? ]]
-=== 4. Access Control and Permissions ===
-The access control and permissions of the software Dataverse are quite flexible. Users have different levels of access control, i.e.:
-  * Dataverse level access control
-  * Dataset level access control
-  * File level access control
-User accounts can be granted **roles**  that define which **actions**  they are allowed to take on specific dataverses, datasets and/or files. And each role comes with **a set of permissions**, which define the **specific actions**  that users may take.
-This means you can also restrict any files in your datasets. Permission refers here to the access of files, as metadata is always visible. If you do restrict file access, **do not restrict a file without any terms**, please **give information about why it’s restricted**, otherwise your data isn’t [[https://www.go-fair.org/fair-principles/|FAIR]].
-The figure below shows an overview of roles and permissions in the software Dataverse.
-{{:fdmathsb:roles.png?direct&900}}
-See also: [[https://guides.dataverse.org/en/latest/user/dataverse-management.html#roles-permissions|Roles & Permissions]]
-<note tip>Using “**Private URL**”. If you are not ready to share your datasets, you can use a private URL, and send it to people who should look at your dataset in advance and who would **review**  your unpublished dataset. It allows persons **without a Dataverse account**  to **access**  your unpublished dataset, and have full read access to that dataset. This feature is only for DRAFT datasets.</note>
-{{:fdmathsb:testdata_with_private_url_2025-03-10.png?linkonly|See also Figure: Private URL}}
-<note tip>
-If you publish a dataset, **all files**  in the dataset **will be published**, if no files have been set for “embargoed” or “restricted”. </note>
-<note>**A published dataset cannot be deleted**, but it can be **deaccessioned**. Deaccessioning a dataset is a very serious action, it only occurs if there is a legal or valid reason for the dataset to no longer be accessible to the public.</note>
-See also:
-  * [[https://guides.dataverse.org/en/latest/user/dataset-management.html#publish-dataset|Publish Dataset]]
-  * [[https://guides.dataverse.org/en/latest/user/dataset-management.html#preview-url-to-review-unpublished-dataset|Preview URL to Review unpublished Dataset]]
-  * [[https://guides.dataverse.org/en/latest/user/dataset-management.html#embargoes|Embargoes]]
-  * [[https://guides.dataverse.org/en/latest/user/dataset-management.html#restricted-files-terms-of-access|Restricted File + Terms of Access]]
-=== 5. Other useful features ===
-  * File hierarchy
-  * File previews [[https://guides.dataverse.org/en/latest/user/dataset-management.html#file-previews|Previewers are available for these file types]]
-  * Versioning [[https://guides.dataverse.org/en/latest/user/dataset-management.html#dataset-versions|Dataset Versions]], [[https://guides.dataverse.org/en/latest/user/dataset-management.html#replace-files|Replace Files]]
-  * Usage statistics and metrics
-  * Guestbook
-  * Faceted search
-  * …
-For more information see [[https://dataverse.org/software-features|Dataverse Features]]
-----
-==== GRO.data Servers ====
-If you feel **confident**  with the data handling and process of data archiving and publishing, feel free to **go to the production server**  of GRO.data, otherwise you can go to the GRO.data **test server to try out**  everything first before putting your data on the production server.
-**Login**  with both of the servers **via academic cloud/AcademicID**, and go to the server website and **follow the login with academic cloud**.
-  * [[https://test.data.gro.uni-goettingen.de/|GRO.data Test Server]]
-  * [[https://data.goettingen-research-online.de|GRO.data Production Server]]
-<note important>Please keep in mind: **Never** publish your **REAL DATA** on the GRO.data **Test** Server, it **should be published on** the GRO.data **Production Server**!</note>
-==== 3. Prepare your data ====
-=== 3.1. Preparation process for archiving and publishing data ===
-The following diagram guides you in **preparing** your data thoroughly and professionally **before** publishing and archiving your data. If you have a lightweight dataset to archive, you might finish your preparation quickly, but **do plan more time** than you suppose to need.
-Once you get everything done, you'll **enjoy** the ease that the repository **software** does lots of **work for you**, e.g. **backup** your **data daily**, and your **published data** is **available** **around-the-clock**, all year round, for all **interested users**.
-{{:fdmathsb:data_preparation_2025-10-07.png?700}}
-References:
-  * [[https://dataverse.org/sites/projects.iq.harvard.edu/files/dataverseorg/files/introduction_to_data_handling_on_the_dataverse_platform.pdf|Data Handling on The Dataverse Project]]
-  * [[https://www.youtube.com/watch?v=DzyFHoZp71g|Introduction to Data Handling on the Dataverse Platform]]
-=== 3.2. File formats: consideration and recommendation for selecting suitable file types and formats for archiving and sharing ===
-Here are the **considerations** and **recommendations** for selecting suitable file formats for archiving and sharing:
-  * Open and non-proprietary data formats when possible: they do not depend on specific non-open software, therefore they would have a high likelihood of long-term sustainability.
-  * File formats that are an international standard for your files.
-  * File formats that commonly used in your research areas, by research communities, or other interested parties.
-  * Preferably data formats, which are human-readable, e.g., plain text files in contrast to binary files.
-  * File formats that have not been developed by a vendor-independent standards organisation or a community, but if the development has stabilised, these formats can be seen as equivalent to open formats, quasi-standard, for practical purpose. One example is TIFF, which is proprietary but widely used and well-documented, another example is the archiving format ZIP.
-  * File formats that are not platform-independent, but they're supported by Windows machines, Macs, and Unix-, Linux-based systems.
-  * Text files should be in ASCII or UTF-8 encoding.
-  * No file formats that are not widely available and understood.
-//Currently//, we recommend the following file types and formats for archiving and sharing: see Table 1.
-{{:fdmathsb:suitable_file_types_and_formats_for_archiving_2025-10-25.png?700}}
-More about **Organising Files**, **File Formats**  and **Document Data**:
-  * [[https://researchdata.org/organizing-data/|Organizing Data]], researchdata.org
-  * [[https://unlimited.ethz.ch/spaces/DD/pages/194127898/Archivtaugliche+Dateiformate#ArchivtauglicheDateiformate-TextFormat|File Formats for Archiving]], ETH Zürich, in German
-  * [[https://forschungsdaten.info/themen/veroeffentlichen-und-archivieren/formate-erhalten/|File Formats]], forschungsdaten.info, in German
-  * [[https://www.loc.gov/preservation/digital/formats/fdd/descriptions.shtml|Sustainability of Digital Formats]], Library of Congress
-  * [[https://harvardwiki.atlassian.net/wiki/spaces/digitalpreservation/pages/48136654/DRS+Format+Recommendations|Harvard Library Digital Preservation]]
-  * [[https://www.dlib.org/dlib/march14/rimkus/03rimkus.print.html|A Study of Digital Preservation File Format]], University of Illinois, 2014
-  * [[https://www.ub.uzh.ch/en/wissenschaftlich-arbeiten/mit-daten-arbeiten/daten-dokumentieren.html|Document Data]], University Library Zurich
-  * [[https://datamgmtinedresearch.com/document|Documentation in Research Data Management]], Crystal Lewis, Data Management in Large-Scale Education Research
-Feel free to take a look at our **Survival Kit**  for research data management on the topics of [[https://www.hs-bremen.de/forschen/forschungsdatenmanagement/fdm-aufbereitung-analyse/|metadata, data security, versioning, data formats]], (short info), with useful links, as well as [[https://www.hs-bremen.de/forschen/forschungsdatenmanagement/fdm-bereitstellung-archivierung/|preparation and publication of data]].
-==== A test Dataset with folders ====
-Here we have a test dataset with folders as an example to show you a possible folder structure. Please feel free to have your own folder structure fitting your dataset, e.g., with **raw data**  and/or **aggregated data**  etc.
-{{:fdmathsb:testdata_with_folder.png?direct&550  }}
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-<note> If you upload a dataset with **folder structure** as a ZIP file, the Dataverse software will **automatically** fill in the file path information for each file contained in the .ZIP file. If there is more than one file in the dataset, and at least one of them has a non-empty directory path, the dataset page will present an option for switching between the traditional table view, and the **tree-like view** of the files showing the folder structure as in the test dataset example. </note>
-==== Example Data Formats for some Special User Groups ====
-To maximise our support and to save your time and work, we collect some special example data and data formats here for your convenience.
-**[[https://www.dicomstandard.org/|DICOM]]** data: Examples from [[https://dataverse.harvard.edu/|Harvard Dataverse]]
-  * [[https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/EN3Q6I|[Jang 2021] DICOM data for sensitivity analysis of radiomic features in CMR]]
-  * [[https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/AI2OXS|[Emmerling 2016] Raw DICOM files/The role of the insular cortex in retaliation]]
-  * [[https://dataverse.harvard.edu/dataverse/harvard?q=DICOM&types=dataverses%3Adatasets%3Afiles&sort=score&order=desc&page=1|More DICOM data examples from Harvard Dataverse]]
-==== GRO.data User Guide ====
-  * {{:fdmathsb:2025-03-30_research_data_repository_gro.data_user_guide_a.pdf|GRO.data User Guide: Part I}}
-  - GRO.data overview
-  - Account and Login
-  - Creating a Dataverse
-  - Adding Dataset
-  * {{:fdmathsb:2025-01-06_research_data_repository_gro.data_users_guide_b.pdf|GRO.data User Guide Part: II}}
-  - How to publish
-  - Roles and permissions
-  - Dataset Terms - License/Data Use Agreement
-  - Data citation
-  - Finding and using data
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-Feel free to visit our website [[https://www.hs-bremen.de/forschen/forschungsdatenmanagement/|Research Data Management at HSB City University of Applied Sciences]], in German.
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-<note>We endeavour to ensure the accuracy of the information on the wiki and provide references for it. Please note that, we cannot guarantee the completeness of the information. We warmly welcome your **feedback**, **comments** and **suggestions**.
-We are committed to ensuring that your intellectual property rights are not infringed upon. In the event that this is indeed the case, please notify us at once.</note>
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-{{:fdmathsb:logo_eu_und_bmftr_2025-06-19.png?300  |BMFTR_en_DTP_CMYK_gef_durch}}