Project Overview
ABOUT

The ATLAS guidelines were developed within the PRIN 2022 ATLAS project, a joint effort of the University of Bologna, the Ca’ Foscari University of Venice, and the CNR Unit of Pisa (comprising the Institute for Computational Linguistics “A. Zampolli”—ILC—and the Institute of Information Science and Technologies “Alessandro Faedo”—ISTI). ATLAS has two main objectives:

  1. creating a catalogue of Digital Humanities Research on Italian Cultural Heritage, using semantic web technologies to establish a framework that captures diverse DH research outputs, and
  2. developing clear and practical guidelines for creating high-quality scholarly data.

For a full presentation of the ATLAS project, its outputs, and methodologies, refer to the whitebook and the following publications:

  • Bardi, Alessia, Marilena Daquino, Riccardo Del Gratta, Angelo Mario Del Grosso, and Roberto Rosselli Del Turco. ’The ATLAS of Italian Digital Humanities: A Knowledge Graph of Digital Scholarly Research on Italian Cultural Heritage’, 11 June 2024. https://doi.org/10.5281/ZENODO.11569280.
  • Daquino, Marilena, Alessia Bardi, Marina Buzzoni, Riccardo Del Gratta, Angelo Mario Del Grosso, Franz Fischer, Francesca Tomasi, and Roberto Rosselli Del Turco. ’The ATLAS: A Knowledge Graph of Digital Scholarly Research on Italian Cultural Heritage’, 2024. https://doi.org/10.6092/unibo/amsacta/7927.

The ATLAS guidelines are built upon the existing recommendations of the ALLEA Working Group for E-Humanities: Sustainable and FAIR Data Sharing in the Humanities.

While the ALLEA guidelines are broad in scope, we created a complementary tool that offers specific, practical advice for applying FAIR principles to common DH research outputs: digital scholarly editions, text collections, software tools, linked open datasets, and ontologies. We identified best practices, reference standards, and tools for implementing FAIR principles in the design, development, and maintenance of these outputs.

These guidelines are primarily aimed at Digital Humanities scholars seeking to learn about or deepen their understanding of FAIR principles, focusing on how to put them into practice when creating their research outputs. Additionally, the guidelines are also suitable for scholars who are (relatively) new to DH and wish to explore this field’s distinctive research products, along with their respective best practices and reference standards.

The guidelines are divided into sections by output type. Each section provides a set of recommendations that cover the phases of a digital object’s life cycle—aligning with the framework proposed in the ALLEA recommendations, as illustrated below.

Figure 1 - Life cycle of FAIR research data in the humanities. Credits: ALLEA E-Humanities Working Group.

To help you identify which ALLEA phases each recommendation corresponds to, you will find the following tags next to each recommendation:

The ATLAS guidelines provide also a set of additional recommendations to enhance the quality of research outputs beyond FAIR principles, making them easier to cite, use, and assess. These recommendations address common shortcomings we discovered while reviewing research outputs.

Before exploring our guidelines, we strongly recommend reading the ALLEA recommendations first and other external resources, especially if you are new to FAIR principles. To help you get started, we provide a concise summary of the ALLEA recommendations below, giving you an overview of the key concepts and helping you identify which areas require deeper study.

IDENTIFY

  • Consider all your research assets as potentially reusable data.
  • Learn the FAIR Data Principles.
  • Document your research digitally from the start.
  • Use recognised tools and browse existing humanities datasets.
  • Aim for data to be as open as possible, as closed as necessary.

PLAN

  • Create a Data Management Plan (DMP) before collecting data.
  • Use funder templates or tools like DMPOnline.
  • Plan for metadata documentation using standard schemas and controlled vocabularies.
  • Keep DMPs updated as research progresses.
  • Involve library/repository staff and consider RDM costs early on.

COLLECT, PRODUCE, STRUCTURE, STORE

Data Types & Formats
  • Choose community-accepted formats and those preferred by preservation repositories.
  • Check what other researchers use for similar data.
Metadata
  • Follow metadata standards and ensure consistency.
  • Use controlled vocabularies, PIDs (Persistent Identifiers), and make metadata rich and machine-readable.
Data Models
  • Apply FAIR principles to data modeling.
  • Use open, human- and machine-readable standards (e.g., XML, RDF).
  • Normalise data and use identifiers like DOI, VIAF.
  • Align data models with the DMP and document thoroughly.
Legal Aspects
  • Address legal issues early, including consent, copyright, and anonymisation.
  • Get legal support from your institution or library.
Licences
  • Identify data ownership before licensing.
  • Prefer open licences (e.g., CC BY, CC0), avoid overly restrictive ones (e.g., NC, ND).
  • Use licence selector tools and make licences machine-readable.
Trusted Repositories & PIDs
  • Use certified repositories (e.g., CoreTrustSeal).
  • Repositories should assign PIDs and allow rich metadata.
  • Link publications and datasets using PIDs.

DISSEMINATE

  • Use networks, portals, and researcher profiles (e.g., ORCID).
  • Share data and supporting materials online.
  • Consider data papers to increase visibility and reuse.
  • Engage broader audiences using non-traditional formats (infographics, apps, exhibitions).
  • Promote open data for education and outreach (e.g., Hackathons).
  • Use trusted repositories for self-archiving.

LEGACY DATA

  • Curate data to prevent it becoming legacy at risk.
  • Address licensing for older data.
  • Advocate for funding for digitisation and infrastructure.
  • Make legacy data open and FAIR whenever possible.