OpenAIRE-Connect: Open Science as a Service for repositories and research communities by Paolo Manghi, Pedro Principe, Anthony Ross-Hellauer, Natalia Manola
Project 2017-19 with 11 partners (technical, research communities, content providers) to extend technological services and networking bridges – creating open science services and building communities. Want to support reuse/reproducibility and transparent evaluation around research literature and research data, during the scientific process and in publishing artefacts and packages of artefacts.
Barriers – repositories lack support (eg integration, links between repositories). OpenAIRE want to facilitate new vision so providing “Open Science as a Service” – research community dashboard with variety of functions and catch-all broker service.
Researchers using random storage solutions and don’t really know what they’re doing. Need to improve their skills. Have been setting up training for various groups in organisation. Software Carpentry for young researchers to make their work more productive and reliable. 2-day workshops which are discipline-specific and well-attended. Now running their own instructor training which allows expanding service. Author carpentry, data carpentry, etc.
Training for research support staff who are first port of call on data management plans, data protection, basic data management. Recently made mandatory by Dept of GeoSciences to attend DMP training.
Expanding library carpentry to national level.
Global community that develops shared APIs for web-based image delivery; implements that in software; to expose interoperabie image content.
Many image repositories are effectively silos. IIIF APIs allows a layer that lets servers talk to each other and allow easier management and better functionality for end-users. Lots of image servers and clients around now so you can mix-and-match your front and back-ends. Can have deep zoom; compare images and more.
Everything created by global community so always looking for more participants. Community groups, technical specification groups eg extending to AV resources, discovery, text granularity (in text annotations). Also a consortium to provide leadership and communication channels.
Data Management and Archival Needs of the Patagonian Right Whale Program Data by Harish Maringanti, Daureen Nesdill, Victoria Rowntree
Importance of curating legacy datasets. World’s longest continuous study of large whale species: 47 years and counting of data. Two problems:
- to identify whales – found the callosities of right whales were unique (number, position, shape) and pattern remained same despite slight change over time. So can take aerial photos when they surface. Data analysed with complicated computer system and compared with existing photos.
- to gather data over a period of times – where to find whales regularly. Discovered whales gather in three places: 1) mothers and calves; 2) males and females; 3) free-for-all.
Collection has tens of thousands b&w negatives; color slides; analysis notebooks; field notes; Access 1996 database records; sightings maps.
Challenges: heterogeneity of data; metadata – including how much can be displayed publically; outdated databases.
Why should libraries care? We can provide continuity beyond life of individual researchers. Legacy data is as important as current data in biodiversity type fields and generally isn’t digitised yet.
China Scientific Data is a multidisciplinary journal publishing data papers – raw data and derived datasets. Submission (of paper and dataset), review (paper review and curation check), peer review, editorial voting.
How to publish:
- Massive data? – on-demand sample data publication: can’t publish the whole set, but publishes a sample (typical, minimum sized) to announce the dataset’s existence
- Complex data? – publish data and supplementary materials together eg background info, software, vocabulary, code, etc. Eg selected font collections for minority languages
- Dynamic data? – eg when updating with new data using same methodology and data quality control. Could publish as new paper but it’s duplicative so published instead as another version with same DOI. Can be good for your citations!
Encourage authors to store data in their repository so its long-term availability is more reliable.
RDM and the IR: Don’t Reuse and Recycle – Reimplement by Dermot Frost, Rebecca Grant
We all have IRs and they’re designed for PDF publications. Research Data Management is largely driven by funder mandates; some disciplines are very good at it, some less so (eg historians claiming “I have no data” – having just finished a large project including land ownership registries from 17th century, georectified etc!)
FAIR (findable, accessible, interoperable, reusable) data concept (primarily machine-oriented ie findable by machines). IRs can’t do this well enough. Technically uploading a zip file is FAIR but time-costly to user.
Instead should find a domain-specific repository (and read the terms and conditions carefully especially around preservation!) Or implement your own institutional data repository (but different scale of data storage can take serious engineering efforts). Follow the Research Data Alliance.
Need to help researcher across the life-cycle. UofQueensland identifying opportunity to support researchers around funding/journal requirements. Used DMP Online but poor uptake due to lack of mandate. UQ Research Data Manager system:
- Record developed by research – active record (not plan though includes project info) which can change over course of project. Simple dynamic form, tailored to researchers, with guidance for each field.
- Storage auto-allocated by storage providers for working data – given a mapped drive accessible by national collaborators (hopefully international soon) using code provided in completing the form.
- [Working on this part] Publish and manage selected data to managed collection (UQ eSpace). Currently manual process filling in form with metadata fields in eSpace. Potential to transfer metadata from RDM system to eSpace.
- Developing procedures to support the system.
Benefits include uni oversight of research in progress, researcher-centric, improves impact/citation, provides access of data to public.
Preserving and reusing high-energy-physics data analyses by Sünje Dallmeier-Tiessen, Robin Lynnette Dasler, Pamfilos Fokianos, Jiří Kunčar, Artemis Lavasa, Annemarie Mattmann, Diego Rodríguez Rodríguez, Tibor Šimko, Anna Trzcinska, Ioannis Tsanaktsidis
Data very valuable – data published even 15 years after funding stopped, and although always building new and bigger colliders, data is still relevant even decades after collected.
Projects involve 3000 people, including high turnover of young researchers. CERN need to capture everything need to understand and rerun an analysis years later – data, software, environment, workflow, context, documentation.
- Invenio (JSON schema with lots of domain-specific fields) to describe analysis
- Capture and preserve analysis elements
- Reusing – need to reinstantiate the environment and execute the analysis on the cloud.
REANA = REusable ANAlyses supports collaboration and multiple scenarios.