1,721,098 research outputs found
Understanding critical software for UKRI digital research infrastructure
No full textThis scoping study explores key issues around the critical software required to deliver the UKRI digital research infrastructure (DRI), and recommends what help UKRI could provide to support their investments. It also suggests next steps for future research to support DRI.In a complex software ecosystem, understanding what software requires support and investment from UKRI is a complicated task. The Software Sustainability Institute (SSI) examined not just a list of software used by infrastructures, but considered a range of other issues. Our research questions were: What software is used by the UKRI DRI infrastructures researched?What should be considered as ‘critical software’ for UKRI Digital Research Infrastructure?What are the risks to critical software for UKRI Digital Research Infrastructures?How might audits support the infrastructures in preparing for and mitigating risks?To begin to address these questions, the SSI conducted a short study over the course of six months starting in November 2024, interviewing staff from 3 Compute infrastructures (ARCHER2, JASMIN, Isambard-AI) and 3 Data infrastructures (Seshat - part of iDAH, DARE-UK, PSDI) to ascertain details of their set up, funding, software stack, and audit/risk assessment processes, as well as their definitions of ‘critical software’ and opinions on the risks of disruption to the running of their infrastructures. Then, two focus groups were conducted with staff and users of the wider Compute and Data DRI community in order to corroborate and expand on the themes that arose in the interviews. The report explores the software stacks of the 6 infrastructures (Appendix B), and provides illustrations of different ‘critical software’. It looks at risks to software and also addresses the role of audits in understanding and preparing for risks to infrastructures. The report indicates how different parameters – types and size of infrastructure, users, available funding, and where the infrastructures were in their life cycle – influence the choices of software in use, and what this means for the risks to the infrastructures
Preliminary analysis of a survey of research software engineers in the UK.
No full textThis paper presents results from a survey conducted on a new role in academia: the Research Software Engineer (RSE). The survey provides much needed demographic information about the education, field, gender, job satisfaction and career plans of the people of RSEs. The community is found to be highly educated, derive mainly from the hard sciences, and to be predominantly male. Respondents report satisfaction in their jobs, but indicate that career progression is both difficult and opaque. This paper supports a continued discussion about the experience of RSEs and recommends further investigation into this important community.<br/
Dataset - Understanding the software and data used in the social sciences
No full textThis is a repository for a UKRI Economic and Social Research Council (ESRC) funded project to understand the software used to analyse social sciences data. Any software produced has been made available under a BSD 2-Clause license and any data and other non-software derivative is made available under a CC-BY 4.0 International License. Note that the software that analysed the survey is provided for illustrative purposes - it will not work on the decoupled anonymised data set. Exceptions to this are: Data from the UKRI ESRC is mostly made available under a CC BY-NC-SA 4.0 Licence. Data from Gateway to Research is made available under an Open Government Licence (Version 3.0). Contents Survey data &amp; analysis: esrc_data-survey-analysis-data.zip Other data: esrc_data-other-data.zip Transcripts: esrc_data-transcripts.zip Data Management Plan: esrc_data-dmp.zip Survey data &amp; analysis The survey ran from 3rd February 2022 to 6th March 2023 during which 168 responses were received. Of these responses, three were removed because they were supplied by people from outside the UK without a clear indication of involvement with the UK or associated infrastructure. A fourth response was removed as both came from the same person which leaves us with 164 responses in the data. The survey responses, Question (Q) Q1-Q16, have been decoupled from the demographic data, Q17-Q23. Questions Q24-Q28 are for follow-up and have been removed from the data. The institutions (Q17) and funding sources (Q18) have been provided in a separate file as this could be used to identify respondents. Q17, Q18 and Q19-Q23 have all been independently shuffled. The data has been made available as Comma Separated Values (CSV) with the question number as the header of each column and the encoded responses in the column below. To see what the question and the responses correspond to you will have to consult the survey-results-key.csv which decodes the question and responses accordingly. A pdf copy of the survey questions is available on GitHub. The survey data has been decoupled into: survey-results-key.csv - maps a question number and the responses to the actual question values. q1-16-survey-results.csv- the non-demographic component of the survey responses (Q1-Q16). q19-23-demographics.csv - the demographic part of the survey (Q19-Q21, Q23). q17-institutions.csv - the institution/location of the respondent (Q17). q18-funding.csv - funding sources within the last 5 years (Q18). Please note the code that has been used to do the analysis will not run with the decoupled survey data. Other data files included CleanedLocations.csv - normalised version of the institutions that the survey respondents volunteered. DTPs.csv - information on the UKRI Doctoral Training Partnerships (DTPs) scaped from the UKRI DTP contacts web page in October 2021. projectsearch-1646403729132.csv.gz - data snapshot from the UKRI Gateway to Research released on the 24th February 2022 made available under an Open Government Licence. locations.csv - latitude and longitude for the institutions in the cleaned locations. subjects.csv - research classifications for the ESRC projects for the 24th February data snapshot. topics.csv - topic classification for the ESRC projects for the 24th February data snapshot. Interview transcripts The interview transcripts have been anonymised and converted to markdown so that it's easier to process in general. List of interview transcripts: 1269794877.md 1578450175.md 1792505583.md 2964377624.md 3270614512.md 40983347262.md 4288358080.md 4561769548.md 4938919540.md 5037840428.md 5766299900.md 5996360861.md 6422621713.md 6776362537.md 7183719943.md 7227322280.md 7336263536.md 75909371872.md 7869268779.md 8031500357.md 9253010492.md Data Management Plan The study's Data Management Plan is provided in PDF format and shows the different data sets used throughout the duration of the study and where they have been deposited, as well as how long the SSI will keep these records.</span
Software and skills for research computing in the UK
Full text linkSoftware and the people who produce it have revolutionised the way that research is conducted, pervading all aspects of the research lifecycle. These emerging tools and techniques require new skills and, often, new forms of research collaboration that combine a variety of professional capabilities. This report delivers a better understanding of the software and skills required in order for research computing in the UK to respond to the challenges it faces over the next five years across three overlapping levels: people, infrastructure, and policy.This study has been funded through the UKRI Digital Research Infrastructure programme, and will contribute to the development of national programmes. It was undertaken between December 2021 and August 2022 by the Software Sustainability Institute, with researchers based at the University of Edinburgh and the University of Southampton, in collaboration with Dr Michelle Barker
Research software engineering accelerates the translation of biomedical research for health
No full textResearch software engineering is central to data-driven biomedical research, but its role is often undervalued and poorly understood
UK Research Software Survey 2014
No full textThis spreadsheet contains the anonymised data collected as part of a survey of UK researchers in their use of research software.
We asked people specifically about “research software” which we defined as:
“Software that is used to generate, process or analyse results that you intend to appear in a publication (either in a journal, conference paper, monograph, book or thesis). Research software can be anything from a few lines of code written by yourself, to a professionally developed software package. Software that does not generate, process or analyse results - such as word processing software, or the use of a web search - does not count as ‘research software’ for the purposes of this survey.”
We contacted 1,000 randomly selected researchers at each of 15 Russell Group universities. From the 15,000 invitations to complete the survey, we received 417 responses – a rate of 3% which is fairly normal for a blind survey. We used Google Forms to collect responses.
The responses have good representation from across the disciplines, seniorities and genders. This is a statistically significant number of responses that can be used to represent the views of people in research-intensive universities in the UK.
An overview of the data is available on the worksheet "Summary data". Responses to questions are ordered by unique respondent ID. Please read the "README" worksheet for additional information about the collection and processing of this data.
This survey data is licensed under a Creative Commons by Attribution licence. Copyright resides with The University of Edinburgh on behalf of the Software Sustainability Institute
How to choose a license for your software
No full text<p>This talk was presented at the: Licensing, Sharing and funding council policy workshop - learn and ask - 14 September 2015 in Cambridge.</p>
<p>http://www.software.ac.uk/news/2015-08-14-licensing-sharing-and-funding-council-policy-workshop-learn-and-ask-14-september-201</p>
<p>The meeting was organised by Marta Teperek from the Research Operations Office, University of Cambridge.</p>
Mapping human regulatory variation using haplotype-resolved data
No full textMapping regulatory variants is a powerful approach for identifying the underlying biological mechanisms that define heritable phenotypes. Although each individual carries two copies of a gene, most studies of regulatory variants, such as those defining expression quantitative trait loci (eQTL), traditionally sum the expression of genes over both chromosomes. This is despite each chromosome carrying different regulatory haplotypes and studies of allele specific expression (ASE) highlighting that the two copies of each gene can be expressed at very different levels. In this study I defined allele-specific expression (ASE) across a European cohort, and using matching genomic data tested for variants that are associated with haplotype-resolved expression levels across individuals, hence potentially providing greater precision in mapping regulatory variants. This approach was generalised into the first R package available for this type of analysis, so that it can be used and expanded upon by the wider community. I illustrate how novel regulatory variants can be identified using this approach relative to standard eQTL analyses and show how it can be expanded to investigate how non-additive interactions between alleles on the two copies of each chromosome potentially shape a gene’s expression. I consequently present a novel approach for defining regulatory variants, a new easy-to-use R package implementing this approach and how it can be used to provide new insights into the complexity of genetic regulation of gene expression
Addressing Research Software Sustainability via Institutes
Full text linkResearch software is essential to modern research, but it requires ongoing human effort to sustain: to continually adapt to changes in dependencies, to fix bugs, and to add new features. Software sustainability institutes, amongst others, develop, maintain, and disseminate best practices for research software sustainability, and build community around them. These practices can both reduce the amount of effort that is needed and create an environment where the effort is appreciated and rewarded. The UK SSI is such an institute, and the US URSSI and the Australian AuSSI are planning to become institutes, and this extended abstract discusses them and the strengths and weaknesses of this approach
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