Dr James O’Sullivan examines 'reproducibility', one of the shortcomings of digital humanities, which uses computers to anaylse complex materials.

Stewards of social science data face a fundamental tension. On one hand, they want to make their data accessible to as many researchers as possible to facilitate new discoveries. At the same time,...

Growth of the open science movement has drawn significant attention to data sharing and availability across the scientific community. In this study, we tested the ability to recover data collected under a particular funder-imposed requirement of public availability. We assessed overall data recovery success, tested whether characteristics of the data or data creator were indicators of recovery success, and identified hurdles to data recovery. Overall the majority of data were not recovered (26% recovery of 315 data projects), a similar result to journal-driven efforts to recover data. Field of research was the most important indicator of recovery success, but neither home agency sector nor age of data were determinants of recovery. While we did not find a relationship between recovery of data and age of data, age did predict whether we could find contact information for the grantee. The main hurdles to data recovery included those associated with communication with the researcher; loss of contact with the data creator accounted for half (50%) of unrecoverable datasets, and unavailability of contact information accounted for 35% of unrecoverable datasets. Overall, our results suggest that funding agencies and journals face similar challenges to enforcement of data requirements. We advocate that funding agencies could improve the availability of the data they fund by dedicating more resources to enforcing compliance with data requirements, providing data-sharing tools and technical support to awardees, and administering stricter consequences for those who ignore data sharing preconditions.

A number of publishers and funders, including PLOS, have recently adopted policies requiring researchers to share the data underlying their results and publications. Such policies help increase the reproducibility of the published literature, as well as make a larger body of data available for reuse and re-analysis. In this study, we evaluate the extent to which authors have complied with this policy by analyzing Data Availability Statements from 47,593 papers published in PLOS ONE between March 2014 (when the policy went into effect) and May 2016. Our analysis shows that compliance with the policy has increased, with a significant decline over time in papers that did not include a Data Availability Statement. However, only about 20% of statements indicate that data are deposited in a repository, which the PLOS policy states is the preferred method. More commonly, authors state that their data are in the paper itself or in the supplemental information, though it is unclear whether these data meet the level of sharing required in the PLOS policy. These findings suggest that additional review of Data Availability Statements or more stringent policies may be needed to increase data sharing.

Bloomberg Philanthropies’ What Works Cities program is letting small cities share and collaborate on evidenced-based solutions to urban problems.

A pilot project worked with seven existing projects funded by the International Development Research Center of Canada (IDRC) to investigate the implementation of data management and sharing requirements within development research projects. The seven projects, which were selected to achieve a diversity of project types, locations, host institutions and subject areas, demonstrated a broad range of existing capacities to work with data and access to technical expertise and infrastructures. The pilot project provided an introduction to data management and sharing concepts, helped projects develop a Data Management Plan, and then observed the implementation of that plan.   In examining the uptake of Data Management and Sharing practice amongst these seven groups the project came to question the underlying goals of funders in introducing data management and sharing requirements. It was established that the ultimate goal was a change in culture amongst grantees. The project therefore looked for evidence of how funder interventions might promote or hinder such cultural change.   The project had two core findings. First that the shift from an aim of changing behaviour, to changing culture, has both subtle and profound implications for policy design and implementation. A particular finding is that the single point of contact that many data management and sharing policies create where a Data Management Plan is required at grant submission but then not further utilised is at best neutral and likely counter productive in supporting change in researcher culture.   As expected, there are significant bottlenecks within research institutions and for grantees in effectively sharing data including a lack of resources and expertise. However, a core finding is that many of the bottlenecks for change relate to structural issues at the funder level. Specifically, the expectation that policy initiatives are implemented, monitored, and evaluated by Program Officers who are the main point of contact for projects. The single most productive act to enhance policy implementation may be to empower and support Program Officers. This could be achieved through training and support of individual POs, through the creation of a group of internal experts who can support others, or via provision of external support, for instance by expanding the services provided by the pilot project into an ongoing support mechanism for both internal staff and grantees.   Other significant findings include: the importance of language barriers and the way in which assumptions of English language in materials, resources, services and systems permeate the entire system; that data infrastructures are poorly served by current funding arrangements and tools, particularly where they are obliged to seek continuing funding through project grants. There are also fundamental questions raised by the status of digital objects as "data". The concept of data is part of a western scientific discourse which may be both incompatible with other cultures, particularly indigenous knowledge systems. More importantly that discourse may be incompatible with values-based approaches that seek to respect indigenous knowledge through a commitment to retaining context.   With the possible exception of the last finding, none of these issues are exclusive to development research. The Development Research context surfaces them more strongly through its greater diversity of goals and contexts. In many ways this project illustrates not that Development Research has particular special needs, but that it is a site that surfaces issues in policy design and implementation deserving of more consideration across the research enterprise.

In this talk, I use Mauss' theory of the gift in order to shift how we think about data sharing and data donation, particularly in the context of biomedical res

One of the top journals in political science makes data-sharing and replication part of the publication process.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

What do you fear most? Ask a normal person and they might say: the dark, a clown attack, or (my personal) spiders with wings. (As a humorous aside, I was once having a philosophical discussion with…

Authors of scientific papers often don’t want to share their data, sometimes claiming that the data are proprietary. But there are solutions to that.

Data sharing rules are vague and institution-specific and permit researchers to erect obstacles that give them effective veto power over use of their data.

Last week I wrote about data ownership, and how focusing on “ownership” might drive you nuts without actually answering important questions about what can be done with data. In that context, I mentioned a couple of times that you (or your funder) might want data to be shared under CC0, but I didn’t clarify what CC0 actually means. This week, I’m back to dig into the topic of Creative Commons (CC) licenses and public domain tools — and how they work with data. People often refer to everything with a CC in front of it as a “Creative Commons license.” […]

European sui generis database right

From how we do science to publishing practices to the sociology of science, there isn’t an aspect of the scientific endeavor that isn’t in flux right now. Long-term readers of Jabberwocky know that…

Chief takeaway: JOURNAL POLICIES MATTER.

Read the latest article version by Richard Smith, Ian Roberts, at F1000Research.