The OpenEdition Data Project: which are the best strategies to create open access to OpenEdition data?
In the context of the OpenEdition Data project, OpenEdition has disclosed the results of a study on the strategies to provide open access to data. The goal of the OpenEdition Lab programme’s research study, as part of the open data movement, is to simplify the access to data on OpenEdition’s various platforms.
There were three steps to the OpenEdition Data project:
- conducting a study on the strategies to provide open access to data
- defining functional specifications for the OpenEdition Data gateway
- providing support in open access to data
The first step focused on creating state-of-the-art open data in the field of scientific publishing along with a data audit in order to analyse OpenEdition’s position in the world of open data and to create an implementation strategy. This step included several stages:
- creating state-of-the-art open data in the field of scientific publishing;
- conducting interviews with users (researchers, data producers, project leaders, etc.) about data from scientific publishing.
- conducting a workshop to define personas with OpenEdition members.
- carrying out a study on OpenEdition’s strategic positioning.
Creating state-of-the-art open data in scientific publishing
Several French and foreign platforms (Persée, Cairn, PLOS, Hindawi, Scopus, Web of Science, I4OC, Sage, Dalloz, HAL, Huma Num, F1000Research.com) were studied to provide a comprehensive overview of open data policies followed by different actors of scientific publishing. This overview was completed by interviews with resource persons (CCSD / HAL, HumaNum/Isidore et Persée) to study the management and impact of open data projects within organisations.
The interviews spotlighted three important aspects to conduct an open data project:
- the projects are founded on a partnership-based approach and must rely on the first data users and requesters;
- improving the user experience through easy-to-use protocols. The OAI-PMH protocols are difficult to understand and semantic web standards require extensive work while they are not widely used;
- the need for user documentation and support must be taken into account in order to develop a community of users around the data sets.
Interviews and personas to better understand the needs of future OpenEdition Data users
To understand the practices and needs of potential OpenEdition Data users, the team proceeded in two ways: by conducting interviews with potential data users and by defining personas in-house to showcase the diversity of potential OpenEdition data users.
The interviews established several elements to understand the practices and needs of open data users:
- conditions of access to data: the interrogated users expressed a preference for raw data, sets of data in the form of “dumps” instead of programming interfaces (APIs) to directly query the database and avoid data subsets. If the API is too restrictive and the data is in too complex of a format, scraping, which involves automatically importing information from a website, may be an efficient alternative to using open data.
- data preview: the visualisation of sample data provides information about what the data contains, its potential and if it will be easy to work with the data or not.
- data documentation: an essential component which must be aimed at data usage instead of data description.
Providing open access to OpenEdition Data?
A data audit was carried out to define the methods to provide open access to OpenEdition data. 8 data sets were identified for the project:
- Full text
- Consultation statistics
- Coverage lists
- List of ubscribed institutions
- OpenEdition’s administrative data
- Indicators of activity
The OpenEdition data project plans on progressively opening the access to data. As a first step, the portal should aggregate and expose the existing data (metadata of documents, OpenEdition Journals coverage lists, OpenEdition Books and Hypotheses, etc.). As a second step, the portal should enable the opening up of high-value-added data.