---
title: "Publishing Data, Mediapipe, AI Good practice"
subtitle: "Service and Support from Project S"
author:
  - name: "Job Schepens"
    affiliation:
      - name: Universität zu Köln
        department: CRC 1252 Prominence in Language
        city: Cologne, Germany
date: "8 July 2026"
date-format: "D MMMM YYYY"
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## Overview

Three parts:

-   **Part 1 — Publishing Data** licensing, FAIR principles, data journals, and choosing a repository (Zenodo, OSF, LAC, etc.)
-   **Part 2 — MediaPipe Group** quick recap of what the group is, and a call for contributions
-   **Part 3 — AI Good Practice in Daily Workflows** workshop at the upcoming CRC 1252 retreat (5–7 October 2026)


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# Part 1 · Publishing Research Data {.section-divider}

### Part 1: Publishing Data

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## Project S {.smaller}

We help with, e.g.: 

-   **Choosing a repository:** Zenodo, OSF, LAC, etc. 
-   **Choosing a License:** choosing a licence, identifying responsible parties (you, PI, UzK)?
-   **Sensitive data:** determining access, secure storage, encryption, GDPR
-   **Data Management Plan:** drafting, reviewing
-   **Data paper:** finding a suitable journal (e.g., JOHD), documentation


::: notes
Speaker Notes:
- audio/video recordings, corpora, and experimental/behavioural data; sensitive-data advice includes steering people away from sciebo for identifiable recordings; DMP support is in line with DFG expectations for CRC 1252.
:::

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## Good Scientific Practice {.smaller}

As open as possibe:

-   **Open Science** Make data usable for reuse and replication
-   **All Rights Reserved** : Without a license, nobody may legally copy, reuse, or redistribute your data, even if it is in a public GitHub repo

As closed as necessary:

-   **Ethics** GDPR, informed consent, and sensitive data

Who Decides? 

-   The person accountable / grant holder / the PI / the PI's 

::: notes
Speaker Notes:

- *visible* is not automatically *reusable*.
- Data is subject to both copyright and the EU database right (protecting the investment in compiling it).
- PIs/institutions may hold rights to data collected under employment or grant contracts.
:::

------------------------------------------------------------------------

## Removing Unnecessary Barriers {.smaller}

**FAIR** principles:

-   **F — Findable:** metadata, DOI
-   **A — Accessible:** clear access conditions 
-   **I — Interoperable:** standard format 
-   **R — Re-useable:** what may others do? + provide documentation 

-   Also sensitive data can be FAIR


------------------------------------------------------------------------

## Data Licenses {.smaller}

**Creative Commons** is the most common licensing framework for research data. It has a modular structure, e.g.: 

-   **CC0 (Public Domain):** "Waiver", waives all copyright and database rights.
-   **BY** (Attribution): Users must give you credit.
-   **SA** (ShareAlike): Adaptations must be licensed under the same terms.
-   **NC** (Non-Commercial): Only non-commercial use is permitted (not recommended).

```         
       CC BY (Attribution)
       CC BY-SA (Attribution-ShareAlike)
       CC BY-NC (Attribution-NonCommercial)
       CC BY-NC-SA (Attribution-NonCommercial-ShareAlike)
```

Licenses for software (e.g., MIT/GPL) don't address database rights, personal-data protection, or citation norms that apply to research data. *We will discuss licences in detail in the next RDM session (July 15th)*

::: notes
Speaker Notes:

- license controls *what others may do* with your data
- others include Open Data Commons (ODC), seperates database structure rights from content rights.
- NC creates compatibility problems (Non-commercial is not clearly defined, cannot combine a CC BY-NC with CC BY into one, no Search Indexing, nc is not fully open, not allowed in schoolbooks, not with commercial publishers, NC is not unrestricted.)
- In the EU, database rights protect investment in collecting data, even if the data lacks creative expression. CC 4.0 explicitly addresses and waives these rights, unlike CC 3.0.

:::

------------------------------------------------------------------------


## Documentation: Data Journals {.smaller}

A **data paper** in a peer-reviewed data journal can help to properly document a dataset. Examples: 

-   **Journal of Open Humanities Data** Ubiquity Press / Brill
-   **Journal of Open Psychology Data** Ubiquity Press / Brill
-   **Research Data Journal for the Humanities and Social Sciences** Brill
-   **Scientific Data** Springer Nature
-   **Data in Brief** Elsevier

::: notes
Speaker Notes:

- A "data paper" describes how a dataset was created and how it can be reused — it does not report new analyses or findings.
- These are peer-reviewed, indexed, and citable, which is a strong incentive for early-career researchers to invest time in preparing data properly.
- JOHD explicitly welcomes linguistics submissions and has run special collections on language dataset reuse — natural venue for CRC 1252 datasets.
:::

------------------------------------------------------------------------



## Storing and Publishing Sensitive Data {.smaller}

GDPR: Extra care in anonymization/pseudonymization, informed consent, and storage: 

-   **Sciebo** (or similar) is not built for sensitive data
-   Consider purpose-build access-controlled repositories (e.g., CLARIN/Language Archive Cologne, Dataverse/TROLLing, etc.).
-   Institutional network drives with an access log
-   **Encryption** 

::: notes
Speaker Notes:

- repositories like LAC supports restr access with access tiers.
- Always reach out to **Project S** / C3RDM / DCH / Data protection officer before collecting data.
- repository controls *where it lives, how long it survives, and how discoverable it is*.
- *storage/handling* vs publishing/licensing
- **Project S can help reviewing, e.g.: consent forms, etc.**.
:::


------------------------------------------------------------------------

## Choosing a Repository {.smaller}

Important criteria include **Visibility (in community)** and **Openness of the License** e.g. OSF does not require an explicit license.

-   **Open Science Framework (OSF)** helpful concepts for **experimental data** (e.g., structured project components, preregistration). Supports **granular licensing** (e.g., project page CC BY, data component CC0). Full project lifecycle. 
-   **Zenodo** — general purpose; funded by the EU, operated by **CERN**.
-   **[lac.uni-koeln.de](https://lac.uni-koeln.de/about/)** — good for **audio-visual data**, including annotations alongside recordings; operated by the **Data Center for the Humanities (DCH)**
-   **[site.uit.no/trolling](https://site.uit.no/trolling/)** — built on the **Dataverse** platform, recommended by journals (e.g., *Glossa*) 


------------------------------------------------------------------------


## Checklist

-   Ask Project S
-   Don't leave a dataset unlicensed
-   As open as possible, as closed as necessary
-   Check GDPR/consent 
-   Use secure storage when necessary
-   Choose a suitable repository
-   Consider a *data paper*

------------------------------------------------------------------------

# Part 2: MediaPipe {.section-divider}

### Part 2: Mediapipe 

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## MediaPipe Special Interest Group {.smaller}

What?

-   Ongoing, informal, discussion and exchange group 
-   Focus on video-based motion tracking of gesture, sign, gaze, etc. using tools such as **MediaPipe**, **OpenPose**, **OpenFace**
-   Original idea is to support projects studying e.g. multimodal prosody and signal, code, and interactional prominence


How?

-   5 meetings in the past 12 months based on interest and available speakers. 
-   Vadim Kimmelman (March 2026), Anna Kuder et al. (Nov 2025), Fabian Eckert & Aviad Albert et al. (Sept 2025), Klym Myslyvyi & Janne Lorenzen et al. (July 2025), and an open discussion after PaPe/EnvisionBox (July 2025).

::: notes
Speaker Notes:

- Point new members to the mailing list link.
- Briefly mention Kimmelman's talk as an example of the kind of methodological exchange the SIG supports — he is PI of an ERC project on universal properties of nonmanual gestures in sign languages.
:::

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## Call for Contributions {.smaller}

::: {.columns}

::: {.column width="65%"}
-   **Propose a talk:** work-in-progress, a problem you're stuck on, etc.
-   **Suggest a speaker:** know someone (internal or external) working on multimodal prosody, gesture, or gaze methods?
-   **Suggest a topic / question:** work on problems together
-   [**Join the mailing list**](https://lists.uni-koeln.de/mailman/listinfo/sfb1252-mediapipe).
:::

::: {.column width="35%"}
![](https://api.qrserver.com/v1/create-qr-code/?size=300x300&data=https://lists.uni-koeln.de/mailman/listinfo/sfb1252-mediapipe)
:::

:::



------------------------------------------------------------------------

# Part 3: AI {.section-divider}

### Part 3: AI

------------------------------------------------------------------------

## AI Good Practice Norms {.smaller}

-   Currently: ad hoc individual practices, for example, is it OK to use AI to summarize papers you haven't read? 
-   **What:** responsible use of LLMs, disclosure and documentation norms, data privacy, AI openness questions.
-   **Starting point:** UzK **KI-Richtlinie** (27 January 2026)
-   **When:** the CRC 1252 retreat, **5–7 October 2026**.

::: notes
Speaker Notes:

- AI as part of everyday workflows (transcription, coding, writing, analysis), the CRC wants shared, informed norms across projects rather than ad hoc individual practices.
- Also connects to CC/FAIR discussion from Part 1.

:::

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## Thanks for your attention! {.smaller}

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