White House Releases Draft Federal Data Strategy Action Plan – SPARC

Yesterday, the White House Office of Management and Budget (OMB) released their long-awaited draft Federal Data Strategy Action Plan which outlines the Administration’s concrete action plan for implementing the President’s Management Agenda priority to leverage data as a National Strategic Asset. It also serves as a blueprint for the government’s implementation of the Foundations for Evidence-Based Policymaking/Open Government Act, which was signed into law in January.

Along with the draft action plan, OMB released final versions of the principles and practices it expects agencies to follow in gathering, using, protecting, and engaging with data.

The draft action plan, which is open for public comment until July 5th, lays out actions considered fundamental for the government to undertake during the first year in order to execute the full breadth of the strategy over time. It includes concrete deliverables for each individual federal agency, as well as government-wide actions facilitated by collaborative agency work.

The plan articulates six actions for all federal agencies to individually complete once the action plan is finalized in August:

  1. Improve data resources for artificial intelligence research and development by February 2020
  2. Constitute a diverse data governance body by September 2019
  3. Assess data and related infrastructure maturity by May 2020
  4. Identify opportunities to increase staff data skills by May 2020
  5. Identify data needed to answer key agency questions by August 2020
  6. Identify priority datasets for agency open data plans by August 2020…”

Towards next-generation data-driven science: policies, practices and platforms

The CODATA 2019 Conference will be held on 19-20 September 2019 in Beijing, China. This year’s conference theme is: Towards next-generation data-driven science: policies, practices and platforms.

The conference will follow a high-level workshop, 17-18 September 2019, on ‘Implementing Open Research Data Policy and Practice’  that will examine such challenges in China and elsewhere in the light of the emergence of data policies and in particular the China State Council’s Notice on ‘Measures for Managing Scientific Data’.

Science globally is being transformed by new digital technologies.  At the same time addressing the major global challenges of the age requires the analysis of vast quantities of heterogeneous data from multiple sources.  In response, many countries, regions and scientific domains have developed Research Infrastructures to assist with the management, stewardship and analysis.  These developments have been stimulated by Open Science policies and practices, both those developed by funders and those that have emerged from communities.  The FAIR principles and supporting practices seek to accelerate this process and unlock the potential of analysis at scale with machines.  This conference provides a significant opportunity to survey and examine these developments from a global perspective.”

Model Licensing Terms and Specifications for Data Resources, Version 2019-05

“This report documents a set of licensing terms and specifications recommended for use when negotiating purchase agreements for large, datasets and databases provided by commercial vendors in the areas of business, financial, and geospatial data. These terms and specifications may be applicable for licenses used in acquiring one-off datasets (raw or structured data absent tools or enabling software) and statistical databases (centrally-hosted data, usually offered through software, systems, and/or tools)….”

Interview with Michael Markie, Director of Publishing at F1000 | Eurodoc

In this interview, Michael Markie, Director of Publishing at F1000, will discuss a new concept for an open publishing platform that aims to facilitate faster, more efficient publishing, as well as making the whole publication process more transparent through Open Data and Open Peer Review….”

Developing a research data policy framework for all journals and publishers

Abstract:  More journals and publishers – and funding agencies and institutions – are introducing research data policies. But as the prevalence of policies increases, there is potential to confuse researchers and support staff with numerous or conflicting policy requirements. We define and describe 14 features of journal research data policies and arrange these into a set of six standard policy types or tiers, which can be adopted by journals and publishers to promote data sharing in a way that encourages good practice and is appropriate for their audience’s perceived needs. Policy features include coverage of topics such as data citation, data repositories, data availability statements, data standards and formats, and peer review of research data. These policy features and types have been created by reviewing the policies of multiple scholarly publishers, which collectively publish more than 10,000 journals, and through discussions and consensus building with multiple stakeholders in research data policy via the Data Policy Standardisation and Implementation Interest Group of the Research Data Alliance. Implementation guidelines for the standard research data policies for journals and publishers are also provided, along with template policy texts which can be implemented by journals in their Information for Authors and publishing workflows. We conclude with a call for collaboration across the scholarly publishing and wider research community to drive further implementation and adoption of consistent research data policies.

From data sharing to data publishing | MNI Open Research

Abstract:  Data sharing, i.e. depositing data in research community accessible repositories, is not becoming as rapidly widespread across the life science research community as hoped or expected. I consider the sociological and cultural context of research and lay out why the community should instead move to data publishing with a focus on neuroscience data, and outline practical steps that can be taken to realize this goal.

Open Science: An Academic Librarian’s Perspective – Open @ CUNY

Open Science is a multifaceted notion encompassing open access to publications, open research data, open source software, open collaboration, open peer review, open notebooks, open educational resources, open monographs, citizen science, or research crowdfunding in order to remove barriers in the sharing of scientific research output and raw data (FOSTER). In other words, the goal of the Open Science movement is to make scientific data a public good in contrast to the expansion of intellectual property rights over knowledge propagated by the paywalled dissemination model. Therefore, Open Science is more of a social and cultural phenomenon aiming to recover the founding principles of scientific research rather than an alternative form of knowledge exchange. It is important to emphasize that despite the fact that Open Science is currently most visible in the area of “hard sciences” (due to large data sets generated by high-throughput experiments and simulations), it is not limited to only the STEM fields — it is also applicable to other types of scientific research….

In order to support open data-driven research, academic librarians have to expand traditional library services and adopt new data-related roles, which will require expanding their qualifications beyond library science and subject degrees toward information technologies, data science, data curation, and e-science. This will lead to a deep transformation in librarians themselves….”

Credit data generators for data reuse

“Much effort has gone towards crafting mandates and standards for researchers to share their data13. Considerably less time has been spent measuring just how valuable data sharing is, or recognizing the scientific contributions of the people responsible for those data sets. The impact of research continues to be measured by primary publications, rather than by subsequent uses of the data….

To incentivize the sharing of useful data, the scientific enterprise needs a well-defined system that links individuals with reuse of data sets they generate4….

A system in which researchers are regularly recognized for generating data that become useful to other researchers could transform how academic institutions evaluate faculty members’ contributions to science….”