Objectives

- Delivery of meaningful collections of data

- Data delivered by the PDL must be:

- Fixed to the checksum level- Discoverable- Usable with confidence without referral to any additional material (Context!)- Assured availability for the foreseeable future

Objectives

- PDL is not a replacement for BODC’s “traditional” data serving

- It is a parallel system

- It is tailored to suit the needs of the academic publishing community

- It does not support “data behind the graph”

Design

- Datasets assigned a DOI through DataCite

- NERC affiliated to DataCite through British Library

- doi prefix = 10.5285

- suffix = a UUID

Design

- DOIs resolve to an HTML landing page

- Landing page contains metadata concerning dataset

- Landing page links off to usage metadata & data

Current status

- Descriptive pages live

- https://www.bodc.ac.uk/data/published_data_library/

Current status

Current status

- Descriptive pages live

- DOI catalogue live – 8 datasets w/ DOIs

- https://www.bodc.ac.uk/data/published_data_library/catalogue

Current status

- Descriptive pages live

- DOI catalogue live – 8 datasets w/ DOIs

- DOI landing pages live

- e.g. https://www.bodc.ac.uk/data/published_data_library/catalogue/41479c42_4dfb_4da9_be97_4c532ce13922/

Current status

- Landing pages contain human & machine readable metadata

- HTML & RDFa

- Updates noted as hAtom entries

- Fields documented in cookbook

Future work

- Currently PDL is entirely hand-coded

- Design documented for an RDBMS back office

- Tables in place- Population started - ironing out issues- Followed by middleware to create web views

Future work

- Links with IODE POD- Test dataset to be ingested by AGU- Prove concept of POD- Link from a BODC landing page to POD repository

Pressure points

Workflow, landing pages, author, editor, publisher, data centre communication and education

Publishing a dataset in a data journal will provide academic credit to data scientists, and without diverting effort from their primary work on ensuring data quality.Funders want to get the best possible science for their money. Running measurement campaigns is expensive, so the more reuse that can be derived from a dataset, the better. Publication in a data journal ensures that the dataset is uploaded to a trusted repository where it will be backed up, archived and curated and so won’t be vulnerable to bit-rot or being lost/stored on obsolete media. The peer-review process also reassures the funder that the published dataset is of good quality and that the experiment was carried out appropriately.

● supported by NERC – in particular the British Atmospheric Data Centre ● partnership formed between Royal Meteorological Society & academic publishers Wiley-Blackwell

● develop a mechanism for the formal publication of data in the Open Access Geoscience Data Journal

● builds on JISC funded OJIMS (Overlay Journal Infrastructure for Meteorological Sciences) project● parallels work done by the NERC Science Information Strategy Data Citation and Publication project

● brings all the NERC environmental data centres together.

Geoscience Data Journal, Wiley-Blackwell and the Royal Meteorological Society

● Publishing a dataset in a data journal will provide academic credit to data scientists, and without diverting effort from their primary work on ensuring data quality.

● Funders want to get the best possible science for their money. Publication in a data journal ensures that the dataset is uploaded to a trusted repository where it will be backed up, archived and curated and so won’t be vulnerable to bit-rot or to being lost/stored on obsolete media.

● The peer-review process also reassures the funder that the published dataset is of good quality and that the experiment was carried out appropriately.

● Data journals will be a good starting point for information for researchers outside the immediate field, about what sort of data is available and how to access the data.

● Data publication will help show transparency in the scientific process, improving public accountability.

● Opportunities to form partnerships with other organisations with the same goal of data publication to exploit common activities and achieve a wider community buy-in. For example, the CODATA-ICSTI Task Group on Data Citation Standards and Practises, DataCite and others.

GDJ Rationale and Incentives

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