# JuDDGES (Judicial Decision Data Gathering, Encoding and Sharing) DMP CHIST-ERA

Candice Fillaud, Chérifa Boukacem-Zeghmouri

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## To cite this version:

Candice Fillaud, Chérifa Boukacem-Zeghmouri. JuDDGES (Judicial Decision Data Gathering, Encoding and Sharing) DMP CHIST-ERA. Université Claude Bernard Lyon 1. 2024. hal-04684763v3

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# JuDDGES (Judicial Decision Data Gathering, Encoding and Sharing) DMP CHIST-ERA

Version 4

## Description

This document corresponds to the Data Management Plan for the JuDDGES (Judicial Decision Data Gathering, Encoding and Sharing) project and aims to apply FAIR principles to data and software produced. It will be updated regularly, at least every semester until the end of the project. Please note that this DMP follows the CHIST-ERA model using the ARGOS tool. Some questions are open-ended and others are closed, with multiple or single choice answers, which explains why some answers are short and lack context.

The overall goal of the JuDDGES project is to harness state-of-the-art Natural Language Processing & Human-In-The-Loop technologies to provide legal researchers with new Open software and tools that enable extensive, flexible and on-going meta-annotation capability (both automated and employing domain experts in-the-loop). This capability is applied to legal records/judgments from criminal courts across jurisdictions with varied legal constitutions (Poland, England & Wales).

The project is organized into 4 WPs :

* ☐ WP1: Project management

* ☐ WP2: Gathering and Human Encoding of Judicial Decision Data

* ☐ WP3: NLP and HITL Machine Learning Methodological Development

* ☐ WP4: Open science practices & engaging early career researchers

More information here: https://juddges-project.eu/
### Funder

CHIST-ERA | | CHIST-ERA

### Grant

Judicial Decision Data Gathering,
Encoding and Sharing
(anr _________::ANR-23-CHRO-
0001)

### Researchers

Łukasz Augustyniak (orcid:0000-0002-4090-4480), Mandeep Dhami (orcid:0000-0001-6157-3142), Krzysztof Kaminski (orcid:0000-0003-2103-371X), Candice FILLAUD (orcid:0009-0005-2084-3384), David Windridge (orcid:0000-0001-5507-8516), Chérifa Boukacem-Zeghmouri (orcid:0000-0002-0201-6159), Albert Sawczyn, Santosh Tirunagari (orcid:0000-0002-9064-1965), Jakub Binkowski, Michał Bernaczyk (orcid:0000-0001-7683-8852), Tomasz Kajdanowicz (orcid:0000-0002-8417-1012)

### Organizations

Middlesex University, Wrocław University of Science and Technology,
Université Claude Bernard Lyon1
# 1. Basic Information

Title: JuDDGES (Judicial Decision Data Gathering, Encoding and Sharing) DMP CHIST-ERA

Description:

This document corresponds to the Data Management Plan for the JuDDGES (Judicial Decision Data Gathering, Encoding and Sharing) project and aims to apply FAIR principles to data and software produced. It will be updated regularly, at least every semester until the end of the project. Please note that this DMP follows the CHIST-ERA model using the ARGOS tool. Some questions are open-ended and others are closed, with multiple or single choice answers, which explains why some answers are short and lack context.

The overall goal of the JuDDGES project is to harness state-of-the-art Natural Language Processing & Human-In-The-Loop technologies to provide legal researchers with new Open software and tools that enable extensive, flexible and on-going meta-annotation capability (both automated and employing domain experts in-the-loop). This capability is applied to legal records/judgments from criminal courts across jurisdictions with varied legal constitutions (Poland, England & Wales).

The project is organized into 4 WPs :

- [ ] WP1: Project management

- [ ] WP2: Gathering and Human Encoding of Judicial Decision Data

- [ ] WP3: NLP and HITL Machine Learning Methodological Development

- [ ] WP4: Open science practices & engaging early career researchers

More information here: https://juddges-project.eu/
**Researchers:**

Łukasz Augustyniak (orcid:0000-0002-4090-4480)

Mandeep Dhami (orcid:0000-0001-6157-3142)

Krzysztof Kaminski (orcid:0000-0003-2103-371X)

Candice FILLAUD (orcid:0009-0005-2084-3384)

David Windridge (orcid:0000-0001-5507-8516)

Chérifa Boukacem-Zeghmouri (orcid:0000-0002-0201-6159)

Albert Sawczyn

Santosh Tirunagari (orcid:0000-0002-9064-1965)

Jakub Binkowski

Michał Bernaczyk (orcid:0000-0001-7683-8852)

Tomasz Kajdanowicz (orcid:0000-0002-8417-1012)

**Affiliations:**

Middlesex University

Wrocław University of Science and Technology

Université Claude Bernard Lyon1

Contact: FILLAUD Candice (candice.fillaud@univ-lyon1.fr)

## 2. License

License: CC-BY-4.0

Access Rights: Public

## 3. Funding

Funder: CHIST-ERA | | CHIST-ERA

Grant: Judicial Decision Data Gathering, Encoding and Sharing (anr _________ ::ANR-23-CHRO-0001)

## 4. Generic Data Questions

## 5. Data Management

### Descriptions

### JuDDGES datasets

The main aim of WP2 is to establish the data to develop and test the tool. The objectives will be to (1) collate/gather openly sourced legal case records and judgments for coding; (2) develop a coding scheme
for extracting data from legal case records and judgments; (3) train human coders to reliably code data from legal case records and judgments using the specially designed coding scheme; (4) make human only coded data available for use by WP3, (5) facilitate human-in-loop coding for WP3, and (6) enable WP4 to make data Open and re-usable by others beyond the project team. It also aims to allow the WP compliant to the standard of Open Publishing.

This is a dataset for Legal informatics combining judgment documents from Poland and UK legal jurisdictions, annotated with rich metadata for advanced information extraction. To date, the datasets produced are those corresponding to objective (1) to (4).

Polish datasets:

☐ **JuDDGES/pl-court-raw:** Raw data from Polish court decisions

☐ **JuDDGES/pl-court-instruct:** Annotated and structured data from Polish court decisions

☐ **JuDDGES/pl-court-graph:** Graphical representations of Polish courts judgements

☐ **JuDDGES/pl-nsa:** The dataset consists of Supreme Administrative Court of Poland judgements available at [orzeczenia.nsa.gov.pl](https://orzeczenia.nsa.gov.pl), containing full content of the judgements along with metadata sourced from the official website.

☐ **JuDDGES/pl-swiss-franc-loans:** Dataset for training and evaluating Large Language Models (LLMs) for information extraction in domain of Polish court judgments regarding Swiss Franc loans cases.

English and Wales datasets:

☐ **JuDDGES/en-court-raw:** Raw data from England and Wales Appeal Court judgements

☐ **JuDDGES/en-court-instruct:** The data was acquired from publicly available judgments from the Court of Appeal (Criminal Division) of England and Wales.

☐ **JuDDGES/en-appealcourt:** Dataset for training and evaluating large language models (LLMs) for information extraction in the domain of publicly available England and Wales Court of Appeal (Criminal Division) judgment
☐ **JuDDGES/en-appealcourt-coded-instruct_v02:** Instruction-format information-extraction examples derived from public Court of Appeal (Criminal Division) judgments of England & Wales

It is planned to possibly modify or version them by adding new metadata or new judgments rather than
creating new datasets.

Template: CHIST-ERA Data Management

Type: Dataset

# 1 Data Description And Collection Or Reuse of Existing Data

## 1.1 What data (for example the kind, formats, and volumes), will be collected or produced?

### 1.1.1 Give details on the kind of data

a. Derived or compiled (e.g., text mining, 3D models)

b. Other

The collected datasets come from digital databases (particularly government databases); they consist of primary digital data (raw data) and secondary data.

☐ **JuDDGES/pl-court-raw:** The dataset consists of Polish Court judgements available at https://orzeczenia.ms.gov.pl/, containing full content of the judgements along with metadata sourced from official API and extracted from the judgement contents. This dataset contains raw data.

☐ **JuDDGES/pl-court-instruct:** The dataset consists of Polish Court judgements available at https://orzeczenia.ms.gov.pl/, containing full content of the judgements along with metadata sourced from official API and extracted from the judgement contents. This dataset is designed for fine-tuning large language models (LLMs) for information extraction tasks and is formatted as instructions.
☐ **JuDDGES/pl-court-graph:** This dataset is primarily based on the JuDDGES/pl-court-raw. The dataset consists of nodes representing either judgments or legal bases, and edges connecting judgments to the legal bases they refer to. Also, the graph was cleaned from small disconnected components, leaving single giant component.

☐ **JuDDGES/pl-nsa:** The dataset consists of Supreme Administrative Court of Poland judgements available at orzeczenia.nsa.gov.pl, containing full content of the judgements along with metadata sourced from the official website. The dataset contains documents up to 2025-03-05, with the last update on 2025-03-06. Some recent documents may be missing. The NSA database is continuously updated, though delays may cause older documents to appear over time.

☐ **JuDDGES/pl-swiss-franc-loans:** Dataset for training and evaluating Large Language Models (LLMs) for information extraction in domain of Polish court judgments regarding Swiss Franc loans cases. Dataset is primarily based on the JuDDGES/pl-court-raw dataset, which is curated from the Polish Court Portal.

☐ **JuDDGES/en-court-raw:** The dataset consists of England and Wales Appeal Court judgements available at https://caselaw.nationalarchives.gov.uk/judgments/advanced_search?court=ewca/crim/, containing full content of the judgements from official website. This dataset contains raw data.

☐ **JuDDGES/en-court-instruct:** The data was acquired from publicly available judgments from the Court of Appeal (Criminal Division) of England and Wales. These licenses encourage the use and re-use of the information available under them freely and flexibly, with only a few conditions.
This dataset is designed for fine-tuning large language models (LLMs) for information extraction tasks and is formatted as instructions. For raw dataset see JuDDGES/en-court-raw.

☐ **JuDDGES/en-appealcourt:** Dataset for training and evaluating large language models (LLMs) for information extraction in the domain of publicly available England and Wales Court of Appeal (Criminal Division) judgments. The coding schema includes 43 annotated codes across several categories of judgments, including:

Sampling judgments/cases

Extracting basic information on court hearings

Extracting basic information on offence, trial, and sentence

Extracting basic information on appeals
The instruction dataset for England and Wales was created using a manual process. Legal experts first defined extraction schemas and coding guidelines in a workshop. We then built an HTML parser to download all 6,154 Criminal Division Court of Appeal judgments (as XML, up to 15 May 2024) under the Crown Copyright and Open Government licences. Yearly word-length statistics (min/25th/median/75th/max) revealed a bi-modal distribution, a few extreme outliers (>40 k words), and an incomplete 2024 tranche.

For curator training, judgments were stratified into five token-length bins, with five cases sampled per bin (110 total; four later removed), yielding 106 candidates. Twenty were used for inter-rater (20 distinct coders) and intra-rater (10 recoded) reliability checks. Once reliability thresholds (>80%) were achieved, two further stratified batches of 800 judgments each were assigned to Curator 1 (531 annotations) and Curator 2 (96 annotations). After careful cleaning, 573 annotated judgments were selected for the final dataset.

□ JuDDGES/en-appealcourt-coded-instruct_v02: The raw data was acquired from publicly available judgments from the Court of Appeal (Criminal Division) of England and Wales. These licenses encourage the use and re-use of the information available under them freely and flexibly, with only a few conditions.

Using the raw dataset a total of 621 judgements have been manually curated by two expert curators across 46 different codes.

This dataset contains instruction-format information-extraction examples derived from public Court of Appeal (Criminal Division) judgments of England & Wales. Each example pairs:

context: the full text of a judgment.

output_1: the human extracted values from the judgement in json.

Output_2: human coded to specific categories that are normalised as well as human extracted values from the judgements in json.

The goal is to fine-tune LLMs to reliably extract structured data—e.g. plea dates, offences, etc.—from lengthy legal texts.

This dataset is designed for fine-tuning large language models (LLMs) for information extraction tasks and is formatted as instructions. For dataset see JuDDGES/en-appealcourt-coded-instruct_v02.
1.1.2 Give details on the data format

The data consists of primary digital data (raw data) and secondary data (for analysis) extracted
via API or HTML parser.

The formats are non-proprietary and open source, such as parquet, JSON, xml and PyG formats.

1.1.3 Justify the use of certain formats

widely supported format across disciplines

The .parquet format is a commonly used format in the discipline. Given that the data is on the HuggingFace platform, it is the most appropriate format (more information [here](https://huggingface.co parquet-format): "Parquet is a columnar storage format optimized for querying and processing large datasets. Parquet is a popular choice for big data processing and analytics and is widely used for data processing and machine learning.").

For this dataset: JuDDGES/pl-court-graph, the JSON format is intended for analysis and contains most of the attributes available in JuDDGES/pl-court-raw. We excluded some less-useful attributes and text content, which can be easily retrieved from the raw dataset and added to the graph as needed. The PyG format is designed for machine learning applications, such as link prediction on graphs, and is fully compatible with the Pytorch Geometric framework (more information [here](https://pytorch.org/docs/stable/stable.html)).

For JuDDGES/en-appealcourt-coded-instruct_v02 and JuDDGES/en-appealcourt: these
judgments are available in HTML format on the national archives website for online reading.
They can be downloaded as XML or PDF files under the crown copyright license: and the Open
Government license.
### 1.1.4 Give details on the volumes

GB (gigabyte)

It depends on the dataset:

- [ ] JuDDGES/pl-court-raw: 10.3 GB

- [ ] JuDDGES/pl-court-instruct: 2.94 GB

- [ ] JuDDGES/pl-court-graph: 1.57 GB (PyG format)/ 250MB (JSON)

- [ ] JuDDGES/pl-court-synthetic-qa: 3.52 MB

- [ ] JuDDGES/pl-nsa: 2.76GB

- [ ] JuDDGES/pl-swiss-franc-loans: 518 MB

- [ ] JuDDGES/en-court-raw: 86.6 MB

- [ ] JuDDGES/en-court-instruct: 138.53 MB

- [ ] JuDDGES/en-appealcourt: 31.8 MB

## 2 Documentation And Data Quality

### 2.1 Documentation And Data Quality

#### 2.1.1 What metadata and documentation will accompany the data?

##### 2.1.1.3 Indicate how the data will be organised during the project

For now, the dataset names follow the naming convention 'project name/country-data type', for example: JuDDGES/pl-court-raw.

##### 2.1.1.4 Consider what other documentation is needed to enable re-use

All the documentation needed is available within the datasets cards.

- [ ] https://huggingface.co/datasets/JuDDGES/pl-court-raw (doi: 10.57967/hf/4698)
☐ https://huggingface.co/datasets/JuDDGES/pl-court-instruct (doi: 10.57967/hf/4699)

☐ https://huggingface.co/datasets/JuDDGES/pl-court-graph (doi: 10.57967/hf/4701)

☐ https://huggingface.co/datasets/JuDDGES/pl-swiss-franc-loans

☐ https://huggingface.co/datasets/JuDDGES/en-court-raw (doi: 10.57967/hf/4700)

☐ https://huggingface.co/datasets/JuDDGES/en-court-instruct (doi: 10.57967/hf/4697)

☐ https://huggingface.co/datasets/JuDDGES/en-appealcourt

☐ https://huggingface.co/datasets/JuDDGES/en-appealcourt-coded-instruct_v02

What the dataset cards contain:

☐ **Description:** An overview of the dataset, including its purpose, potential applications, and history.

☐ **Composition:** Details about dataset features such as the number of rows, data types, specific column characteristics, etc.

☐ **Usage:** Information on how the dataset can be used, including code examples, specific tasks it is suited for, and recommended configurations.

☐ **Creation:** Information on how the dataset was created, including data sources, collection process, cleaning methods, and authors or contributors.

☐ **Ethical Considerations:** Discussions on ethical considerations related to dataset use, such as potential biases, privacy impacts, and precautions to take.

☐ **License:** Information on usage rights and the license under which the dataset is published.

☐ **References:** Citations and references to related works or articles associated with the dataset.

The data will also be directly linked to the publications; the detailed methodology in the publications will help complete the metadata.
# 3 Reused Data

## 3.1 Reused Data

### 3.1.2 Where can re-used data be found?

The datasets created, including raw data, were compiled from national open legal databases (see 1.1).

### 3.1.3 Which data will be re-used?

Aside from the raw datasets initially created, the project does not reuse data already available in a repository.

# 4 Storage And Backup During The Research Process

## 4.1 Storage And Backup During The Research Process

### 4.1.1 How will data security and protection of sensitive data be taken care of during the research?

#### 4.1.1.1 Explain how the data will be recovered in the event of an incident and describe the main risks and how these will be managed

The data is stored in databases, institutional servers, and platforms such as Hugging Face. Multiple backups are implemented to prevent data loss.

During our research activities, we must ensure that data and metadata are stored securely and backed up regularly to prevent any loss or corruption. To this end, we have chosen MongoDB Atlas as our primary storage solution.

MongoDB Atlas Database Storage: MongoDB Atlas provides a fully managed cloud database service that offers high availability, scalability, and compliance with the most stringent data security standards. Our datasets and corresponding metadata will be hosted on MongoDB Atlas clusters, which are configured for optimal performance and reliability.

**Backup Strategy:** The backup protocol within MongoDB Atlas is robust and automated. We will perform backups of our entire dataset every 6 hours without fail. This frequent backup schedule ensures that in the event of an unexpected system failure or data corruption incident, so we can quickly restore the most recent version of our data with minimal loss.

The backups themselves are snapshots of the database state at consistent points in time.

These snapshots include all collections, documents, indexes, and configurations present when each backup is taken. They are encrypted using advanced encryption methods to protect sensitive information from unauthorized access during transit and at rest.
In addition to our rigorous backup strategy within MongoDB Atlas, we will periodically create new versions of datasets for integration with the Hugging Face platform. This process involves exporting curated collections from our MongoDB database and transforming them into formats suitable for machine learning models hosted on Hugging Face.

Dataset Versioning for Hugging Face: We deliberately create dataset versions to track changes over time and provide researchers with access to historical and up-to-date data. By versioning our datasets, we facilitate reproducibility in research by enabling comparisons across different iterations of the same dataset.

Significant updates or enhancements made to the existing collections in MongoDB Atlas will determine the frequency at which these new versions are created. Each version released on Hugging Face will be thoroughly documented, detailing any modifications or additions since the previous release. This ensures transparency and provides users with clear insights into the evolution of the dataset.

By maintaining an active presence on platforms like Hugging Face, we contribute valuable resources to a broader community and invite collaboration and feedback that can lead to further improvements in our work. Our commitment extends beyond just storing and backing up data; it encompasses sharing knowledge and fostering advancements within the field through accessible, high-quality datasets.

The UK datasets are currently stored on both MongoDB and Hugging Face, with future updates and backups planned across these platforms.

# 5 Legal And Ethical Requirements, Codes Of Conduct

## 5.1 Legal And Ethical Requirements, Codes Of Conduct

### 5.1.2 How will other legal issues, such as intellectual property rights and ownership, be managed? What legislation is applicable?

#### 5.1.2.1 Data ownership and accessibility

##### 5.1.2.1.1 Who will be the owner(s) of the data?

a. Wroclaw University of Technology

Łukasz Augustyniak (orcid: 0000-0002-4090-4480)

b. Wroclaw University of Technology

Jakub Binkowski

c. MIDDLESEX UNIVERSITY

David Windridge (orcid: 0000-0001-5507-8516)
5.1.2.1.2 Explain what access or restrictions will apply to the data?

* Open

* Restricted

Some datasets are already available to the community and have been assigned a DOI. Other datasets that are currently under development and documentation, are only accessible to partners of the JuDDGES project (Middlesex University, Wrocław University of Science and Technology and Université Claude Bernard Lyon 1). Access is via the Hugging Face platform with restricted access and accounts for sharing. Otherwise, access to the data is restricted to university partners working on the datasets, which are stored on MongoDB (Middlesex University and Wrocław University of Science and Technology).

Password

5.1.2.2 Intellectual property rights

5.1.2.2.1 Explain which intellectual property and how will they be dealt with

Other

Not relevant

5.1.3 Ethical issues

5.1.3.1 What ethical issues and codes of conduct are there, and how will they be taken into account?

Ethical issues surrounding personal data

A letter of approval regarding the ethical considerations of the project was issued on March 8, 2024 for all the documents such as Informed Consent Form, Data Protection Declaration, Participant Recruitment Information.

☐ As mentioned in the project proposal:

'The research will first undergo ethics review from the Department of Psychology Research Ethics Committee at Middlesex University London, followed by reviews from Wroclaw and
Lyon where necessary and applicable. The University has a strict data protection and declaration procedure which adheres to 7 principles, namely, that the information is Fairly and lawfully processed, processed for specified and lawful purposes, adequate, relevant and not excessive, accurate and kept up to date where necessary, not kept for longer than is necessary, kept secure, and necessary to actively demonstrate compliance with all of the above principles. The University abides by GDPR rules and exemptions. DHAMI has previously been granted ethics approval for student projects involving human encoding of court records/legal judgments including from the BAILII data resource and so we do not foresee any ethical issues.'

# 6 Data Sharing And Long-term Preservation

## 6.1 Data Sharing And Long-term Preservation

### 6.1.1 How and when will data be shared? Are there possible restrictions to data sharing or embargo reasons?

#### 6.1.1.1 Explain how the data will be discoverable and shared

* Deposit in a FAIR-enabling data repository

* Indexed in a catalogue

#### 6.1.1.2 Outline the plan for data preservation and give information on how long the data will be retained

The data is currently available on Hugging Face.

#### 6.1.1.3 Explain when the data will be made available

Most of the datasets are already available on Hugging Face under Attribution 4.0 International (CC BY 4.0) license or under Open Government Licence https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/ depending on the dataset.

#### 6.1.1.4 Indicate the expected timely release

2024-12-22

#### 6.1.1.5 Will exclusive use of the data be claimed?

No
6.1.1.8 Is it necessary to restrict access to certain communities or to apply a data sharing agreement?

No

6.1.2 How will data for preservation be selected, and where data will be preserved long-term?

6.1.2.1 Indicate what data must be retained or destroyed for contractual, legal, or regulatory purposes

Retained

To be decided and updated.

6.1.2.2 Indicate how it will be decided what data to keep

To be decided and updated.

6.1.2.3 Describe the data to be preserved long-term

Other

To be decided and updated.

6.1.2.4 Indicate where the data will be deposited

* Repository of University of Wroclaw

* Hugging Face Hub

It has not been decided yet.

6.1.2.5 Indicate how the data will be shared

Repository

6.1.2.6 Indicate whether potential users need specific tools to access and (re-)use the data.

The data will be provided in open formats as much as possible to ensure unrestricted reuse.

6.1.3 How will the application of a unique and persistent identifier to each data set be ensured?

6.1.3.1 What type of persistent identifier (PID) will be used?

DOI
# 7 Data Management Responsibilities And Resources

## 7.1 Data Management Responsibilities And Resources

### 7.1.1 Who will be responsible for data management?

#### 7.1.1.1 Outline the roles and responsibilities for data management/stewardship activities

a. Łukasz Augustyniak (orcid: 0000-0002-4090-4480)

Project Data Manager and Supervisor (PL datasets)

b. Santosh Tirunagari (orcid: 0000-0002-9064-1965)

Data Manager and Supervisor (UK datasets)

c. Candice FILLAUD (orcid: 0009-0005-2084-3384)

Coordinator and author of the data management plan

d. David Windridge (orcid: 0000-0001-5507-8516)

Data Manager and Supervisor (UK datasets)

### 7.1.2 What resources will be dedicated to data management and ensuring that data will be FAIR (Findable, Accessible, Interoperable, Re-usable)?

#### 7.1.2.1 Explain how the necessary resources to prepare the data for sharing/preservation have been costed in

Infrastructure Grant
# Open Science surveys

The main aim of WP4 is to implement the Open Science policy of the call and engage with relevant groups of Early Career Researchers (ECRs) who could benefit from the project's findings and outputs. To this end, we will conduct two surveys: one to understand Open Science practices within the legal scholarship community, and one to understand Open Science practices within the CHIST-ERA community. The results of these surveys will inform our next outputs and deliverables.

Template: CHIST-ERA Data Management

Type: Dataset

## 1 Data Description And Collection Or Reuse of Existing Data

1.1 What data (for example the kind, formats, and volumes), will be collected or produced?

1.1.1 Give details on the kind of data

Primary data

Observational (e.g., sensor data, data from surveys)

1.1.2 Give details on the data format

As part of WP4, we will collect data through a survey of the legal research community. The aim of this survey is to determine the level of knowledge about open science within the international legal scholarship community. Our aim is to identify what open science practices are currently adopted by the community.

The data collected will be in paper format, then digitised and compiled with digital data in open, non-proprietary formats, notably csv.

1.1.3 Justify the use of certain formats

widely supported format across disciplines
Survey data is tabular data, and the most open format is CSV, which is the format we will use for our data.

#### 1.1.4 Give details on the volumes

MB (megabyte)

The survey data we collect will not be large, only a few MB.

## 1.2 How will new data be collected or produced?

1.2.1 Explain which methodologies or software will be used if new data are collected or produced or if third party data are used

Survey data will be collected from two sources: paper survey and digital survey via the SurveyMonkey survey platform.

# 2 Documentation And Data Quality

## 2.1 Documentation And Data Quality

2.1.1 What metadata and documentation will accompany the data?

2.1.1.1 Indicate which metadata will be provided to help others identify and discover the data

Descriptive

2.1.1.2 Indicate which metadata standards will be used

The DDI (Data Documentation Initiative) or Datacite standard.

2.1.1.3 Indicate how the data will be organised during the project

The data will be retrieved in paper format, directly digitised and stored on servers at the University of Lyon 1 dedicated to the JuDDGES project. The data will then be analysed using open source tools such as framacalc or analysed on Grist (a platform developed by the MESR). Once analysed, documented and cleaned, we plan to make it available on European or disciplinary data repositories.

2.1.1.4 Consider what other documentation is needed to enable re-use

In order to reuse and understand the data, we will add documentation and publish the questionnaire structure and the data collected during the survey. Regarding the linked documentation, we will provide a dictionary explaining each variable.
# 3 Reused Data

## 3.1 Reused Data

### 3.1.2 Where can re-used data be found?

No data will be reused.

### 3.1.3 Which data will be re-used?

No data will be reused.

# 4 Storage And Backup During The Research Process

## 4.1 Storage And Backup During The Research Process

### 4.1.1 How will data security and protection of sensitive data be taken care of during the research?

#### 4.1.1.1 Explain how the data will be recovered in the event of an incident and describe the main risks and how these will be managed

The data collected will not contain any identifying, sensitive or confidential information. However, it will be stored on the servers of Université Claude Bernard Lyon 1 and backed up on the project's cloud storage.

# 5 Legal And Ethical Requirements, Codes Of Conduct

## 5.1 Legal And Ethical Requirements, Codes Of Conduct

### 5.1.1 Personal data

#### 5.1.1.1 Are there any personal data to be formulated?

No

#### 5.1.1.2 Explain whether there is a managed access procedure in place for authorised users of personal data

No personal data will be collected.

### 5.1.2 How will other legal issues, such as intellectual property rights and ownership, be managed? What legislation is applicable?

#### 5.1.2.1 Data ownership and accessibility

##### 5.1.2.1.1 Who will be the owner(s) of the data?

Claude Bernard University Lyon 1

* Candice FILLAUD (orcid: 0009-0005-2084-3384)

* Mandeep Dhami (orcid: 0000-0001-6157-3142)
* Chérifa Boukacem-Zeghmouri (orcid: 0000-0002-0201-6159)

5.1.2.1.2 Explain what access or restrictions will apply to the data?

Open

The data will be made available once it has been processed and documented.

5.1.2.2 Intellectual property rights

5.1.2.2.1 Explain which intellectual property and how will they be dealt with

Other

Not relevant

5.1.3 Ethical issues

5.1.3.1 What ethical issues and codes of conduct are there, and how will they be taken into account?

Other

With regard to the data collected, we are in contact with the Legal and Institutional Affairs Division for the investigation in order to take all necessary legal precautions.

# 6 Data Sharing And Long-term Preservation

## 6.1 Data Sharing And Long-term Preservation

6.1.1 How and when will data be shared? Are there possible restrictions to data sharing or embargo reasons?

6.1.1.1 Explain how the data will be discoverable and shared

Deposit in a FAIR-enabling data repository

6.1.1.2 Outline the plan for data preservation and give information on how long the data will be retained

The data will be open and stored in a data repository in accordance with FAIR principles. Group members will have deposit rights to ensure that they have control over the deposit at the end of the project.

6.1.1.3 Explain when the data will be made available

The data will be made available as soon as it has been sufficiently documented.

6.1.1.4 Indicate the expected timely release

2025-10-31
6.1.1.5 Will exclusive use of the data be claimed?

No

6.1.1.7 Indicate how the data will be used

The data will be used for analyses for journal articles.

6.1.1.8 Is it necessary to restrict access to certain communities or to apply a data sharing agreement?

No

6.1.2 How will data for preservation be selected, and where data will be preserved long-term?

6.1.2.3 Describe the data to be preserved long-term

Observational (e.g., sensor data, data from surveys)

6.1.2.4 Indicate where the data will be deposited

Open Science Framework

The data will be deposited on Zenodo or on Open Science Framework (OSF) in a file dedicated to the JuDDGES project.

6.1.2.5 Indicate how the data will be shared

Repository

6.1.2.6 Indicate whether potential users need specific tools to access and (re-)use the data.

The data format is open and interoperable, and no specific software is required.

6.1.3 How will the application of a unique and persistent identifier to each data set be ensured?

6.1.3.1 What type of persistent identifier (PID) will be used?

DOI

# 7 Data Management Responsibilities And Resources

## 7.1 Data Management Responsibilities And Resources

7.1.1 Who will be responsible for data management?

7.1.1.1 Outline the roles and responsibilities for data management/stewardship activities

Candice FILLAUD (orcid: 0009-0005-2084-3384)

Survey data management, drafting of the DMP
7.1.2 What resources will be dedicated to data management and ensuring that data will be FAIR (Findable, Accessible, Interoperable, Re-usable)?

7.1.2.1 Explain how the necessary resources to prepare the data for sharing/preservation have been costed in

* Use of national infrastructure

* Use of institution infrastructure

* Infrastructure Grant
# 6. Software Management

Descriptions

JuDDGES web application

The app is designed for annotating judgmental documents. It utilises a pre-annotation mechanism that speeds up annotation work for law experts.

Template: CHIST-ERA Software Management

Type: Software

## 1 Accessibility / License

### 1.1 Accessibility / License

#### 1.1.1 How can the software be accessed by third parties?

Publicly available (e.g. GitHub, via URL, etc.)

#### 1.1.2 Software without a license cannot be used by others. Does your software have a license?

Yes

#### 1.1.3 If yes, what is the license of your software?

The software also requires a Label Studio instance (license: Apache-2.0 license).

## 2 Documentation

### 2.1 Documentation

#### 2.1.1 What type of documentation is available, provided with the software and delivered under the same conditions?

* User documentation

* README file
* Other

2.1.2 Is the purpose of the software stated in the documentation?

Yes

2.1.3 Does the documentation describe how to test the software?

The documentation is in progress.

# 3 Testing

## 3.1 Testing

3.1.2 Are sample data and/or parameters that can be used to test the software available with the source code?

The app can be tested on our data prepared specifically for this app. The procedure for preparing the data is implemented in the code repository.

# 4 Interoperability

## 4.1 Interoperability

4.1.1 Do you use existing and standard input/output formats?

Yes

The app uses standard JSON format for input and output.

# 5 Versioning

## 5.1 Versioning

5.1.1 Do you use a version control system?

Yes

Git

# 6 Reproducibility

## 6.1 Reproducibility

6.1.1 Do you provide releases of your software?

Yes

6.1.2 How do you define language-specific dependencies of your software and their version?

NA
# 7 Recognition

## 7.1 Recognition

7.1.1 Do you include citation information (i.e. how to cite your software in the form of citation.cff, codemeta.json or bibtex)?

Yes
# JuDDGES data gathering and model training

Repository containing codebase for:

☐ Accessing Polish Court API and fetching data from it

☐ Storing data in vector search database for further retrieval

☐ Analysis of gathered data

☐ Fine-tuning and evaluation of Large Language Models (LLMs) on dataset prepared with HITL tool

GitHub: https://github.com/pwr-ai/JuDDGES

Template: CHIST-ERA Software Management

Type: Software

## 1 Accessibility / License

### 1.1 Accessibility / License

#### 1.1.1 How can the software be accessed by third parties?

Publicly available (e.g. GitHub, via URL, etc.)

## 2 Documentation

### 2.1 Documentation

#### 2.1.1 What type of documentation is available, provided with the software and delivered under the same conditions?

* README file

* Release notes

* Docstring/comments in the source code

* Other
2.1.2 Is the purpose of the software stated in the documentation?

Yes

2.1.3 Does the documentation describe how to test the software?

Yes

2.1.5 Does the documentation describe how to build the software?

NA

2.1.6 Does the documentation describe how to deploy the software?

NA

2.1.7 Does the documentation describe how to install the software?

NA

# 3 Testing

## 3.1 Testing

3.1.1 What type of testing do you use?

* Unit

* Other (e.g.)

3.1.2 Are sample data and/or parameters that can be used to test the software available with the source code?

- unit tests written to automatically test crucial part of the code

- scripts with use case examples

# 4 Interoperability

## 4.1 Interoperability

4.1.1 Do you use existing and standard input/output formats?

Yes

The data is stored in standard formats, including CSV, JSON
# 5 Versioning

## 5.1 Versioning

5.1.1 Do you use a version control system?

Yes

Git

# 6 Reproducibility

## 6.1 Reproducibility

6.1.1 Do you provide releases of your software?

Yes

# 7 Recognition

## 7.1 Recognition

7.1.1 Do you include citation information (i.e. how to cite your software in the form of
citation.cff, codemeta.json or bibtex)?

Yes

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