https://openworking.wordpress.com/2018/06/26/workshop-report-software-reproducibility-how-to-put-it-into-practice/

Workshop Report: Software Reproducibility – How to put it into practice?

Read the blog post but these are some of the highlights:

Key discussion points and insights on the advice by Hut, van de Giesen & Drost

Lack of funding for Research Software Engineers

Lack of (sustainable) funding for hiring RSEs is one of the obstacles to putting the advice of Hut, Van de Giesen and Drostinto practice. Larger projects typically already have RSEs on board, but for smaller projects this is not always possible. It is difficult to recruit and hire RSEs across disciplines. However, the Netherlands eScience Center is a good example of a way to centrally fund research software development and to pool developer expertise across disciplines.

Open source software is not always an option

Because of scientific competition, commercial and IP interests, it is not always an option to make research software available as open source software. Dockers (containers) are also not an option for commercial software.

Documentation

High-level documentation is very important. A good README file does part of the job, but documentation and a user manual are also important. Any information (e.g. equations, model) behind the software also needs to be shared.

Software validation

Lack of support for software validation is also a problem. As an addition to the advice by Hut, van de Giesen and Drost, one of the groups suggested that support should also be provided for software validation (in-house code review). In cases where professional software support is limited, it would already be helpful if researchers would review each others’ code, just like they would do with papers. If the goal is to make code understandable to other researchers, then their feedback will be paramount. Organizing code reviews in a research group could improve the quality of the code significantly with only a small time investment.

Photo by Hitesh Choudhary on Unsplash

The role of data stewards, RSEs and researchers

Data stewards – the link between researchers and RSEs?

Two groups saw the role of data stewards as brokers between researchers and RSEs. It was acknowledged that researchers and RSEs should interact more to improve research codes (e.g. review of codes). Data stewards could be the link between the two. Data stewards could monitor possible synergy between projects and link researchers with specialist RSE expertise. One group felt that data stewards should provide the toolbox, with principles (e.g FAIR principles) and guidance, and RSEs should help implement those principles, because they have the knowledge to do so.

Could RSEs do more to promote best practices?

Two groups thought that RSEs could take a more proactive role in providing training for researchers, promoting best practices, and generally propagating their knowledge. Without assigning roles, one of the groups felt that implementing the advice of Hut, Van de Giesen and Drost required programming courses, support staff to help out researchers at departmental level, and the breakdown of problems into smaller problems that could be solved with up-to-date techniques based on expert knowledge. Could RSEs also help with this?

Opportunities and barriers, and the role of institutions

Integrated teams working across university faculties, departments, and institutes, with a single point of contact, could provide a way for researchers, data stewards, and RSEs to work together. Fear of stepping into others’ “working areas” and different working cultures may create barriers, as well as the potential lack of scientific/research expertise from RSEs and software developers.

Sustainable funding is a challenge, so is the lack of recognition for developing research software in the current academic rewards system. There also needs to be a persuasive driver beyond just doing the right thing. This can come from funders, publishers and possibly institutions. Any driver will be most persuasive when it comes from the research community itself.

Universities and institutes should promote good practice for software engineering as part of open science.

Next steps

Read the whole blog post here

Data sharing in PLOS ONE: An analysis of Data Availability Statements

By Lisa M. Federer et al.

Published 2 May 2018

https://doi.org/10.1371/journal.pone.0194768

Abstract

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.

It’s Time to Make Your Data Count!

From : https://makedatacount.org/2018/06/05/its-time-to-make-your-data-count/

One year into our Sloan funded Make Data Count project, we are proud to release Version 1 of standardized data usage and citation metrics!

As a community that values research data it is important for us to have a standard and fair way to compare metrics for data sharing. We know of and are involved in a variety of initiatives around data citation infrastructure and best practices; including Scholix, Crossref and DataCite Event Data. But, data usage metrics are tricky and before now there had not been a group focused on processes for evaluating and standardizing data usage. Last June, members from the MDC team and COUNTERbegan talking through what a recommended standard could look like for research data.

MORE: 

Mapping the PID Landscape

Taken from : https://orcid.org/blog/2018/06/07/mapping-pid-landscape

This post was co-authored with Christoper Brown and Neil Jacobs (Jisc), Josh Brown and Laure Haak(ORCID), and Clifford Tatum (SURF)

The landscape of research information is largely closed to us. We rely on original research to solve many of the challenges facing humanity, to improve lives, and to advance human understanding, and we invest in it accordingly. However, when we survey the map of our research world it is filled with gaps. We pass along a few well-trodden roads (too often paying a substantial toll for the privilege) and we can only wonder about what lies just over the horizon.

We can point to many contributing factors: business models that militate against the sharing of information; aggregation of research analytics for local strategic purposes; technological barriers to linking information between sources; cultural practices that reward and privilege a small slice of research activity; and systems that emphasise hard sciences and anglophone literature. Any and all of these can, and do, hide some of the richness of research endeavour. However, these systemic challenges are not the focus of this discussion. Instead, our focus is on the gaps in our understanding of the landscape: the empty parts of the research map.

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Ten reasons to share your data

Making data available to the larger scientific community has many benefits.

27 April 2018

By Amanda S. Barnard

Nature Index:

https://www.natureindex.com/news-blog/ten-reasons-to-share-your-data