Last updated: 2026-03-16
- Research Output Management - VHP4Safety
Help you organise your research data (+ context) in a way it can be reused and shared.
Why?
- Reuse and integration of datasets across the project
- Scientific integrity/transparency: access to data underlying research results
Figure 1: Reproducible science workflow highlighting that reproducible science does not require more time, but more time upfront.
Get your dataset or model listed on the VHP4Safety platform. This will allow you to share your data with the project and the wider scientific community, and to reference your data in your publications. We index this data on BioStudies or Zenodo and friends so the archive's longevity is secured and we can provide you with persistent direct object identifier (doi).
The ultimate goal is to enable open, transparent, and reusable toxicological science that directly supports regulatory decision‑making. By making datasets, models, and their full scientific context FAIR (Findable, Accessible, Interoperable, Reusable), we aim to close the gap between academic research, collaborative projects, and regulatory application. Open and well‑documented toxicology data:
- Enables independent verification and reproducibility of scientific findings
- Facilitates cross‑study integration and model reuse, reducing duplication of effort
- Supports the development, validation, and regulatory acceptance of New Approach Methodologies (NAMs)
- Increases trust, traceability, and transparency in safety assessments
By publishing datasets and models on platforms such as VHP4Safety, research outputs become citable, discoverable, and usable beyond their original context—ultimately accelerating the transition toward animal‑free, data‑driven, and regulator‑ready toxicology that benefits science, industry, and public health alike.
Different repositories are suited to different types of research output. Choosing the appropriate repository helps ensure that data and models are findable, reusable, and interpreted in the correct context. Zenodo: BioStudies – Structured repository for experimental life‑science data, well suited for in vitro and toxicology studies; curated models may fit better in BioModels.
- BioStudies: Structured repository for experimental life‑science data, well suited for in vitro and toxicology studies. Curated models may fit better in BioModels.
- Zenodo: General‑purpose repository for datasets, software, scripts, reports, and mixed outputs.
- FAIRDOMHub: Supports systems biology and modelling workflows, linking models, data, and protocols.
- etc.
The principles underlying this approach are decades old and deeply embedded in academic and laboratory research practice. Since at least the 1970s, frameworks such as Good Scientific Practice (GSP) at universities and Good Laboratory Practice (GLP) in medical and toxicological research have required researchers to systematically record, retain, and document raw data, methods, protocols, and contextual information to ensure traceability, integrity, and reproducibility. GLP was formalized by regulatory bodies such as the U.S. Food and Drug Administration (FDA) and the OECD precisely to address problems arising from incomplete records and irreproducible safety studies, and it explicitly mandates the archiving of all materials needed to reconstruct a study.12 University‑level GSP and research‑integrity guidelines similarly emphasize long‑term stewardship of data and documentation, even when reuse by others was not yet a goal.3 FAIR and open‑science practices therefore do not redefine what good research requires; rather, they build on these long‑standing foundations by making existing good practice more explicit, structured, and sustainable in a digital context.4 If this feels like a substantial change, it is not a criticism of past work but an opportunity to modernize workflows in ways that often reduce long‑term effort, support collaboration, and increase the impact and longevity of research outputs—particularly in medical and toxicological science. For a complete overview of of what is expected within the GSP/GLP vs FAIR framework, see Table A1 in the Appendix.
| Object | Findable under GSP | How |
|---|---|---|
| Paper / article | ✅ Yes | Journals, PubMed, Web of Science |
| Authors | ✅ Yes | Affiliations, ORCID |
| Main conclusions | ✅ Yes | Abstract, discussion |
| Experimental data | ❌ No (usually) | “Available upon request” |
| Code / models | ❌ No | Often not published |
| Parameters / versions | ❌ No | Buried in text or missing |
| Negative results | ❌ No | Rarely published |
Research output includes not only (experimental) data files, but also computational models, protocols, lab notebooks, analysis scripts, metadata, and any other information needed to reuse, reproduce, or interpret your work, whether it originates from in vitro experiments or in silico modelling.5
Tip
We encourage you to share all relevant research output related to your dataset and/or model.
Use the Research Output Inventory link to announce your dataset exists.
- Decide at what level you want to make the data repository entry
- As a general recommendation, it is easiest to align your datasets with the scope of your papers. A paper may consist of multiple assays that produce data — in this scenario, the data of all assays would be packaged together in one dataset.
- Define a clear and descriptive title
- This will be the title for your BioStudies entry (or other repository) and will be displayed on platform.vhp4safety.nl
- Fill out the other fields as much as possible, including the expected public release date (if applicable) and the contact person for this dataset
| Column | Description | Expected value |
|---|---|---|
title |
Title of the dataset; becomes the repository entry title and is displayed on platform.vhp4safety.nl | Free text — clear and descriptive |
identifier |
Assigned Public URL or persistent identifier (DOI, BioStudies accession) once deposited | URL / DOI |
privateLink |
Internal link to the dataset before public release (e.g. BioStudies private key URL) | URL |
caseStudy |
VHP4Safety case study this dataset relates to | kidney / thyroid / parkinson / other (multiple allowed) |
regulatoryQuestion |
Regulatory question addressed by this dataset | Q1 / Q2 (multiple allowed) |
workPackage |
Work Package within the VHP4Safety project | e.g. WP1.1, WP2.1, WP3.1 |
workflowStep |
Step in the VHP4Safety workflow — see glossary.vhp4safety.nl | External exposure / Internal exposure / Toxico(kinetics) / Toxicodynamics/AOP / Hazard / Adverse Outcome (multiple allowed) |
status |
Current sharing status | See Status table below |
embargoDate |
Date until which the entry remains private | YYYY-MM-DD |
license |
Data license applied to the dataset | e.g. CC0, CC BY 4.0 |
hasMetadata |
Whether a metadata template has been filled out | No / Yes |
hasToxTemp |
Whether a ToxTemp template has been completed (cell-based assays only) | No / Started / Yes |
hasROCrate |
Assigned when an RO-Crate package has been created | No / Yes |
contactPerson |
Name of the person responsible for this dataset | Firstname Lastname |
partnerCode |
Partner organisation code of the contact person | e.g. RIVM, UU, UM |
type |
Type of research output | See Type table below, an entry is always a dataset, multiple types can be combined |
isReviewed |
Whether the entry has been peer-reviewed | No / Ongoing / Yes |
Notes |
Any additional remarks or context | Free text |
Status
| Status | Description |
|---|---|
| 🔴 Not shared | The dataset/model exists only on your local computer and has not been uploaded anywhere. |
| 🟠 Partially shared | Some files have been uploaded and shared with the project, but the dataset/model is not yet complete. |
| 🟡 Shared | Uploaded to the VHP Teams shared folder and visible to project members. |
| 🔵 Private | Deposited in a repository (e.g. BioStudies) but not yet publicly visible; the embargo date has not passed. |
| 🟢 Public | Publicly available in a repository and accessible to anyone. |
Type
| Type | Description | Schema mapping |
|---|---|---|
Dataset |
A collection of raw, processed, or derived data files describing one or more topics of interest | schema:Dataset |
Study |
A structured grouping of related assays or experiments sharing a common subject or research question | |
BioAssay |
A specific biological test or measurement procedure producing qualitative or quantitative data | |
BioModel |
A computational or mathematical model representing a biological system or process | bioschemas:ComputationalWorkflow / schema:SoftwareSourceCode |
BioSample |
A biological sample, cell line, or barrier model used in the study | bioschemas:BioSample |
SoftwareApplication |
A software tool, pipeline, or application used to generate or analyse data | schema:SoftwareApplication / schema:SoftwareSourceCode |
ScholarlyArticle |
A peer-reviewed publication associated with the dataset or model | schema:ScholarlyArticle |
AdverseOutcomePathway |
An AOP entry describing a sequence of causally linked biological events (e.g. from AOP-Wiki) |
An entry is always tagged as Dataset, but can be associated with one or more additional types for context. For example, a dataset describing an assay would be tagged as Dataset + BioAssay, while a computational model would be tagged as Dataset + BioModel.
After finishing step one by announcing your dataset, in this step we help you organizing your dataset. A dataset = one main folder consisting of files (and possibly subfolders with additional files).
Files, including but not limited to:
- Protocols
- Lab Notebooks
- Slides
- Raw data
- Processed data
- Analysis scripts
- Metadata
- Manuscript
- And other pieces of information needed to reuse, reproduce or interpret the data
Important
Provide every folder (main and eventual subfolders) with a README.txt to briefly explain in plain language what the folder is about and how the files/subfolders relate to each other.
- We recommend organising your folder according to an ISA (Investigation, Study, Assay) structure:
- Investigation — high-level concept to link related studies
- Study — the central unit, containing information on the subject under study, its characteristics and any treatments applied (a study has associated assays)
- Assay — test performed either on material taken from the subject or on the whole initial subject, which produce qualitative or quantitative measurements (data)
Example: Investigation = WP2.4, Study = TH-DNT-NeuronalCellLines, Assay = THuptake
Structure Example:
your-investigation/ ← investigation root
├── investigation_metadata.xlsx
├── README.txt
│
└── studies/
└── your-study-name/ ← study level
├── study_metadata.xlsx
├── README.txt
├── resources/ ← starting materials, external data
├── protocols/ ← study-level protocols
│
└── assays/ ← assays nested inside their study
├── your-assay-name/
│ ├── assay_metadata.xlsx
│ ├── README.txt
│ ├── protocols/
│ └── dataset/
└── your-second-assay-name/
├── assay_metadata.xlsx
├── README.txt
├── protocols/
└── dataset/ ← outputs of computational analyses (optional)
Example:
TH-DNT-NeuronalCellLines/ ← investigation root
├── investigation_metadata.xlsx
├── README.txt
│
└── studies/
└── TH-DNT-NeuronalCellLines/ ← study level
├── study_metadata.xlsx
├── README.txt
├── resources/
├── protocols/
│ ├── 20251114_cell_culture_protocol_H4.docx
│ └── 20251114_cell_culture_protocol_MO3.13.docx
│
└── assays/ ← assays live inside the study
├── deiodinase/
│ ├── assay_metadata.xlsx
│ ├── README.md
│ ├── protocols/
│ │ ├── 3.3_Bradford_protocol.xlsx
│ │ └── 4.1_Deiodinase_activity_assay.docx
│ └── dataset/
│ ├── characterisation/
│ │ ├── 20221019_H4_MO_SK_D2_D3/
│ │ │ ├── Deiodinase_activity_assay_19-10-22.xlsx
│ │ │ └── raw_data/
│ │ ├── 20230301_H4_MO_SK_D2_D3/
│ │ │ ├── Deiodinase_activity_assay_SK_MO_H4_01-03-2023.xlsx
│ │ │ └── raw_data/
│ │ ├── 20230503_SK_D2_D3/
│ │ ├── 20230510_H4_MO_D2_D3/
│ │ └── Combined_deiodinase_data_EDCs.xlsx
│ └── EDCs/
├── metabolism/
│ ├── assay_metadata.xlsx
│ ├── README.txt
│ ├── protocols/
│ └── dataset/
├── TH_uptake/
│ ├── assay_metadata.xlsx
│ ├── README.txt
│ ├── protocols/
│ └── dataset/
└── TR_activation/
├── assay_metadata.xlsx
├── README.txt
├── protocols/
└── dataset/
Important
File and Folder Name Conventions:
- If you need separators, use
thyroid_caseorthyroid-case— use them consistently - No spaces and avoid special characters (see incompatible characters)
- Keep names short but specific
README files:
- Add a
README.txtper folder
If you do not have a metadata template yet, use the MinimalMetadataTemplate-v1.2 to describe your assay(s).
This metadata template consists of the following modules (tabs), considered relevant for toxicology research:
- General Information
- Biological Model Information
- Chemical Information
- Exposure Information
- Endpoint Readout Information
- Analysis and Statistics
Each module has fields that may be relevant to you. You can ignore fields that are not relevant or add fields to your liking. By working with these modules you should be able to inherit modules for your different assays, avoiding duplication of effort.
Important
Save as yourassayname_assay_metadata.xlsx in the assay folder.
ToxTemp Assistant: https://toxtempassistant.vhp4safety.nl/
- This is for cell-based toxicological methods (assays)
- We do not expect you to be able to fill this template in one go
- You can provide files from your study and assay folder as context for the ToxTemp — the ToxTempAssistant will output a prefilled ToxTemp for you to review
Important
Add the ToxTemp to your folder at the level of the assay.
- Upload to VHP Teams link to folder, set to status to 'shared' in the inventory link.
- Your dataset will be packaged into a Research Object (RO)-Crate and uploaded to BioStudies (or another repository).
- Your entry will remain private until the public release date, you will be able to update the dataset before and the public release date.
- Reference your data in the data availability statement of your paper through a persistent identifier (e.g. DOI, BioStudies accession number) once the dataset is public.
| Aspect | Description of what it entails | GLP / GSP | FAIR | Change comment |
|---|---|---|---|---|
| Scope of research output | Data, protocols, notebooks, scripts, models, metadata | ✅ | ✅ | No change in scope; FAIR makes this scope explicit and visible |
| Data retention | Preserve original data and records over time | ✅ | ✅ | FAIR relies on repositories to ensure durability |
| Documentation | Describe methods, parameters, assumptions | ✅ | ✅ | FAIR requires documentation to support external users |
| Reproducibility | Enable study reconstruction if needed | ✅ | ✅ | FAIR promotes reproducibility by default, not on request |
| Provenance & traceability | Track origin, versions, changes | ✅ | ✅ | FAIR makes provenance machine‑readable |
| Effort profile | When effort is invested | ✅ (late) | ✅ (early) | FAIR shifts effort upfront to reduce long‑term cost |
| Standard formats & vocabularies | Use consistent structures and terminology | ◐ | ✅ | FAIR formalizes and standardizes this |
| Accessibility | Clear conditions for accessing data | ◐ | ✅ | FAIR requires explicit access rules, even if restricted |
| Findability | Ability to discover data/models | ◐ | ✅ | FAIR introduces persistent identifiers and indexing |
| Reuse by others | Enable use beyond the original project | ❌ | ✅ | New expectation under FAIR |
| Machine readability | Structured for computational use | ❌ | ✅ | New requirement driven by FAIR |
| Citation & credit | Enable formal citation of outputs | ❌ | ✅ | FAIR introduces DOIs and formal credit mechanisms |
Legend: ✅ expected, ◐ partially expected / implicit, ❌ not expected
Footnotes
-
OECD. Principles of Good Laboratory Practice (1997, revised).
https://ntp.niehs.nih.gov/sites/default/files/iccvam/suppdocs/feddocs/oecd/oecd_glpcm.pdf ↩ -
U.S. Food and Drug Administration (FDA). 21 CFR Part 58 – Good Laboratory Practice for Nonclinical Laboratory Studies.
https://www.fda.gov/regulatory-information/search-fda-guidance-documents/good-laboratory-practice-nonclinical-laboratory-studies ↩ -
Harvard University. Retention and Maintenance of Research Records and Data.
https://research.harvard.edu/research-records-and-data-retention ↩ -
Nature Scientific Data. Guidelines for Research Data Integrity (2024).
https://www.nature.com/articles/s41597-024-04312-x ↩ -
Jente Houweling, Egon Willighagen. (2023). Research Output Management. Qeios. https://doi.org/10.32388/ZNWI7T. ↩