Research Software Sustainability
The document discusses the importance of research software and challenges in ensuring its sustainability. It notes that research software is increasingly essential in research but often lacks proper maintenance. Three key points are made:
1) Research software is widely used across many fields and agencies invest billions in its development, yet researchers are not rewarded for its creation and maintenance.
2) Without maintenance, research software will collapse over time as it becomes outdated or broken. Many projects rely on just one or two developers.
3) Changing incentives, career paths, training, and funding models is needed to improve the sustainability of research software for the long-term benefit of science.
ICSME 2016 keynote: An ecosystemic and socio-technical view on software maint...Tom Mens
These are the slides of my ICSME 2016 keynote, presented on 5 October 2016 in Raleigh, North Carolina. I focus on the difficulties of maintaining and evolving software ecosystems, large collections of interacting software components that are maintained by a large and active community of contributors and that evolve together in the same environment. Software ecosystems are becoming ubiquitous due to the omnipresence of open source software. I present several problems that arise during maintenance and evolution of software ecosystems, and I argue how some of these challenges should be addressed by adopting a socio-technical view and by relying on a multidisciplinary and mixed methods research approach. I illustrate this with examples of social network analysis, complex systems research, ecological biodiversity, and survival analysis.
Socio-technical evolution and migration in the Ruby ecosystemTom Mens
Presentation by Eleni Constantinou (joint work with Tom Mens, Software Engineering Lab, UMONS) at the BENEVOL 2016 Software Evolution Research Seminar, Utrecht University, The Netherlands
Social and Technical Evolution of the Ruby on Rails Software EcosystemTom Mens
Presentation by Eleni Constantinou (postdoctoral researcher at the Software Engineerin Lab of the University of Mons, Belgium) during the Workshop on Ecosystem Architecuters (WEA2016), Copenhagen, Denmark, 29 November 2016.
Abstract: Software ecosystems evolve through an active community of developers who contribute to projects within the ecosystem. However, development teams change over time, suggesting a potential impact on the evolution of the technical parts of the ecosystem. The impact of such modifications has been studied by previous works, but only temporary changes have been investigated, while the long-term effect of permanent changes has yet to be explored. In this paper, we investigate the evolution of the ecosystem of Ruby on Rails in GitHub in terms of such temporary and permanent changes of the development team. We use three viewpoints of the Rails ecosystem evolution to discuss our preliminary findings: (1) the base project; (2) the forks; and (3) the entire ecosystem containing both base project and forks.
'Scikit-project': How open source is empowering open science – and vice versaNathan Shammah
Open-source pipelines are accelerating scientific discovery, by empowering not only reproducibility of research results but also generalizability of methods. I address the rise of open source in scientific research in quantum physics and quantum information and introduce `scikit-project` a cookbook with best practices for (data) scientists.
See also https://github.com/Machine-Learning-Tokyo/MLT_Talks
ICSME 2016 keynote: An ecosystemic and socio-technical view on software maint...Tom Mens
These are the slides of my ICSME 2016 keynote, presented on 5 October 2016 in Raleigh, North Carolina. I focus on the difficulties of maintaining and evolving software ecosystems, large collections of interacting software components that are maintained by a large and active community of contributors and that evolve together in the same environment. Software ecosystems are becoming ubiquitous due to the omnipresence of open source software. I present several problems that arise during maintenance and evolution of software ecosystems, and I argue how some of these challenges should be addressed by adopting a socio-technical view and by relying on a multidisciplinary and mixed methods research approach. I illustrate this with examples of social network analysis, complex systems research, ecological biodiversity, and survival analysis.
Socio-technical evolution and migration in the Ruby ecosystemTom Mens
Presentation by Eleni Constantinou (joint work with Tom Mens, Software Engineering Lab, UMONS) at the BENEVOL 2016 Software Evolution Research Seminar, Utrecht University, The Netherlands
Social and Technical Evolution of the Ruby on Rails Software EcosystemTom Mens
Presentation by Eleni Constantinou (postdoctoral researcher at the Software Engineerin Lab of the University of Mons, Belgium) during the Workshop on Ecosystem Architecuters (WEA2016), Copenhagen, Denmark, 29 November 2016.
Abstract: Software ecosystems evolve through an active community of developers who contribute to projects within the ecosystem. However, development teams change over time, suggesting a potential impact on the evolution of the technical parts of the ecosystem. The impact of such modifications has been studied by previous works, but only temporary changes have been investigated, while the long-term effect of permanent changes has yet to be explored. In this paper, we investigate the evolution of the ecosystem of Ruby on Rails in GitHub in terms of such temporary and permanent changes of the development team. We use three viewpoints of the Rails ecosystem evolution to discuss our preliminary findings: (1) the base project; (2) the forks; and (3) the entire ecosystem containing both base project and forks.
'Scikit-project': How open source is empowering open science – and vice versaNathan Shammah
Open-source pipelines are accelerating scientific discovery, by empowering not only reproducibility of research results but also generalizability of methods. I address the rise of open source in scientific research in quantum physics and quantum information and introduce `scikit-project` a cookbook with best practices for (data) scientists.
See also https://github.com/Machine-Learning-Tokyo/MLT_Talks
Working towards Sustainable Software for Science: Practice and Experience (WS...Daniel S. Katz
This was a short talk about the WSSSPE events, given at the Dagstuhl workshop on Engineering Academic Software, 20 June 2016. It mostly discusses the working groups that have formed gradually over the WSSSPE meetings, and specifically those that worked through WSSSPE3, and what that have done since then.
A talk about the "Working towards Sustainable Software for Science: Practice and Experience (WSSSPE)" community/theme/set of workshop, focused on WSSSPE3, the working groups that were formed there, how they have developed from activities in previous WSSSPE3 meetings, and their current status.
This talk was given as a Dagstuhl meeting on Engineering Academic Software (http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=16252) 20 June 2016.
Requiring Publicly-Funded Software, Algorithms, and Workflows to be Made Publ...Daniel S. Katz
A presentation made to OECD's Committee for Scientific and Technological Policy (CSTP) at the Workshop on the Revision of the Recommendation of the Council concerning Access to Research Data from Public Funding, 15 October 2019
Scientific Software Challenges and Community ResponsesDaniel S. Katz
a talk given at RTI International on 7 December 2015, discussing 12 scientific software challenges and how the scientific software community is responding to them
How different groups think about software sustainability, what "equations" we might use to measure it, and how it really can't be measured looking forward but only predicted.
Discussing Software Citation and related topics at Workshop on Data and Software Citation (June 6-7 at Harvard Medical School, http://www.software4data.com/#!nsf-workshop/jghgb)
(a slightly updated version of this talk is at https://doi.org/10.6084/m9.figshare.10301741.v1)
A talk on the role of software in research and how NCSA is responding in terms of people and roles - given at the 2019 Data Science Leadership Summit (https://sites.google.com/msdse.org/datascienceleadership2019/).
This is partially based on a previous paper: Daniel S. Katz, Kenton McHenry, Caleb Reinking, Robert Haines, "Research Software Development & Management in Universities: Case Studies from Manchester's RSDS Group, Illinois' NCSA, and Notre Dame's CRC", 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science)
doi: https://doi.org/10.1109/SE4Science.2019.00009
preprint: https://arxiv.org/abs/1903.00732
Working towards Sustainable Software for Science (an NSF and community view)Daniel S. Katz
This talk looks at the goal of sustainable scientific software from the point-of-view of an NSF program officer who funds software as infrastructure, meaning software that enables a community beyond the developers to perform research, and from the point-of-view of the attendees of the First Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE1, http://wssspe.researchcomputing.org.uk/wssspe1/). Issues to be discussed include what sustainability means, funding, incentives, career paths, and communities.
Slides for:
"Software Citation in Theory and Practice," by Daniel S. Katz and Neil P. Chue Hong (published paper - https://doi.org/10.1007/978-3-319-96418-8_34; preprint - https://arxiv.org/abs/1807.08149), presented at International Congress on Mathematical Software (ICMS 2018)
Abstract. In most fields, computational models and data analysis have become a significant part of how research is performed, in addition to the more traditional theory and experiment. Mathematics is no exception to this trend. While the system of publication and credit for theory and experiment (journals and books, often monographs) has developed and has become an expected part of the culture, how research is shared and how candidates for hiring, promotion are evaluated, software (and data) do not have the same history. A group working as part of the FORCE11 community developed a set of principles for software citation that fit software into the journal citation system, allow software to be published and then cited, and there are now over 50,000 DOIs that have been issued for software. However, some challenges remain, including: promoting the idea of software citation to developers and users; collaborating with publishers to ensure that systems collect and retain required metadata; ensuring that the rest of the scholarly infrastructure, particu- larly indexing sites, include software; working with communities so that software efforts count; and understanding how best to cite software that has not been published.
EarthCube Monthly Community Webinar- Nov. 22, 2013EarthCube
This webinar features project overviews of all EarthCube Awards (Building Blocks, Research Coordination Networks, Conceptual Designs, and Test Governance), followed by a call for involvement, and a Q&A session.
Agenda:
EarthCube Awards – Project Overviews
1.. EarthCube Web Services (Building Block)
2. EC3: Earth-Centered Community for Cyberinfrastructure (RCN)
3. GeoSoft (Building Block)
4. Specifying and Implementing ODSIP (Building Block)
5. A Broker Framework for Next Generation Geoscience (BCube) (Building Block)
6. Integrating Discrete and Continuous Data (Building Block)
7. EAGER: Collaborative Research (Building Block)
8. A Cognitive Computer Infrastructure for Geoscience (Building Block)
9. Earth System Bridge (Building Block)
10. CINERGI – Community Inventory of EC Resources for Geoscience Interoperability (BB)
11. Building a Sediment Experimentalist Network (RCN)
12. C4P: Collaboration and Cyberinfrastructure for Paleogeosciences (RCN)
13. Developing a Data-Oriented Human-centric Enterprise for Architecture (CD)
14. Enterprise Architecture for Transformative Research and Collaboration (CD)
15. EC Test Enterprise Governance: An Agile Approach (Test Governance)
A Call for Involvement!
A talk about citation and reproducibility in software, presented at the HSF (High Energy Physics Software Foundation) meeting at SDSC, San Diego, CA, USA, 23 January 2017
Based on citation work done by the FORCE11 Software Citation Working Group as well as recent reproducibility discussions, blogs, and papers
Software Citation: Principles, Implementation, and ImpactDaniel S. Katz
A talk about Software Citation Principles for the 3:am conference (Bucharest, Romania, 28 September 2016), as developed by Arfon M. Smith, Daniel S. Katz, Kyle E. Niemeyer, and the FORCE11 Software Citation Working Group
Big Data Analytics of Software Ecosystem Health: Presentation during INFORTECH Scientific Day (23 May 2018) by Professor Tom Mens. The talk reports on ongoing research of the Software Engineering Lab of the University of Mons (UMONS) on health aspects of evolving software ecosystems. This research was conducted in collaboration with postdoctoral researchers Alexandre Decan and Eleni Constantinou, as well as the external partners of two ongoing research projects: SECOHealth (https://secohealth.github.io) and the Excellence of Science research project SECO-ASSIST (https://secoassist.github.io).
Parsl: Pervasive Parallel Programming in PythonDaniel S. Katz
a seminar presented at the School of Computer Science at the University of St Andrews 18 October 2019 (see https://blogs.cs.st-andrews.ac.uk/csblog/2019/09/25/daniel-katz-parsl/)
Working towards Sustainable Software for Science: Practice and Experience (WS...Daniel S. Katz
This was a short talk about the WSSSPE events, given at the Dagstuhl workshop on Engineering Academic Software, 20 June 2016. It mostly discusses the working groups that have formed gradually over the WSSSPE meetings, and specifically those that worked through WSSSPE3, and what that have done since then.
A talk about the "Working towards Sustainable Software for Science: Practice and Experience (WSSSPE)" community/theme/set of workshop, focused on WSSSPE3, the working groups that were formed there, how they have developed from activities in previous WSSSPE3 meetings, and their current status.
This talk was given as a Dagstuhl meeting on Engineering Academic Software (http://www.dagstuhl.de/en/program/calendar/semhp/?semnr=16252) 20 June 2016.
Requiring Publicly-Funded Software, Algorithms, and Workflows to be Made Publ...Daniel S. Katz
A presentation made to OECD's Committee for Scientific and Technological Policy (CSTP) at the Workshop on the Revision of the Recommendation of the Council concerning Access to Research Data from Public Funding, 15 October 2019
Scientific Software Challenges and Community ResponsesDaniel S. Katz
a talk given at RTI International on 7 December 2015, discussing 12 scientific software challenges and how the scientific software community is responding to them
How different groups think about software sustainability, what "equations" we might use to measure it, and how it really can't be measured looking forward but only predicted.
Discussing Software Citation and related topics at Workshop on Data and Software Citation (June 6-7 at Harvard Medical School, http://www.software4data.com/#!nsf-workshop/jghgb)
(a slightly updated version of this talk is at https://doi.org/10.6084/m9.figshare.10301741.v1)
A talk on the role of software in research and how NCSA is responding in terms of people and roles - given at the 2019 Data Science Leadership Summit (https://sites.google.com/msdse.org/datascienceleadership2019/).
This is partially based on a previous paper: Daniel S. Katz, Kenton McHenry, Caleb Reinking, Robert Haines, "Research Software Development & Management in Universities: Case Studies from Manchester's RSDS Group, Illinois' NCSA, and Notre Dame's CRC", 2019 IEEE/ACM 14th International Workshop on Software Engineering for Science (SE4Science)
doi: https://doi.org/10.1109/SE4Science.2019.00009
preprint: https://arxiv.org/abs/1903.00732
Working towards Sustainable Software for Science (an NSF and community view)Daniel S. Katz
This talk looks at the goal of sustainable scientific software from the point-of-view of an NSF program officer who funds software as infrastructure, meaning software that enables a community beyond the developers to perform research, and from the point-of-view of the attendees of the First Workshop on Sustainable Software for Science: Practice and Experiences (WSSSPE1, http://wssspe.researchcomputing.org.uk/wssspe1/). Issues to be discussed include what sustainability means, funding, incentives, career paths, and communities.
Slides for:
"Software Citation in Theory and Practice," by Daniel S. Katz and Neil P. Chue Hong (published paper - https://doi.org/10.1007/978-3-319-96418-8_34; preprint - https://arxiv.org/abs/1807.08149), presented at International Congress on Mathematical Software (ICMS 2018)
Abstract. In most fields, computational models and data analysis have become a significant part of how research is performed, in addition to the more traditional theory and experiment. Mathematics is no exception to this trend. While the system of publication and credit for theory and experiment (journals and books, often monographs) has developed and has become an expected part of the culture, how research is shared and how candidates for hiring, promotion are evaluated, software (and data) do not have the same history. A group working as part of the FORCE11 community developed a set of principles for software citation that fit software into the journal citation system, allow software to be published and then cited, and there are now over 50,000 DOIs that have been issued for software. However, some challenges remain, including: promoting the idea of software citation to developers and users; collaborating with publishers to ensure that systems collect and retain required metadata; ensuring that the rest of the scholarly infrastructure, particu- larly indexing sites, include software; working with communities so that software efforts count; and understanding how best to cite software that has not been published.
EarthCube Monthly Community Webinar- Nov. 22, 2013EarthCube
This webinar features project overviews of all EarthCube Awards (Building Blocks, Research Coordination Networks, Conceptual Designs, and Test Governance), followed by a call for involvement, and a Q&A session.
Agenda:
EarthCube Awards – Project Overviews
1.. EarthCube Web Services (Building Block)
2. EC3: Earth-Centered Community for Cyberinfrastructure (RCN)
3. GeoSoft (Building Block)
4. Specifying and Implementing ODSIP (Building Block)
5. A Broker Framework for Next Generation Geoscience (BCube) (Building Block)
6. Integrating Discrete and Continuous Data (Building Block)
7. EAGER: Collaborative Research (Building Block)
8. A Cognitive Computer Infrastructure for Geoscience (Building Block)
9. Earth System Bridge (Building Block)
10. CINERGI – Community Inventory of EC Resources for Geoscience Interoperability (BB)
11. Building a Sediment Experimentalist Network (RCN)
12. C4P: Collaboration and Cyberinfrastructure for Paleogeosciences (RCN)
13. Developing a Data-Oriented Human-centric Enterprise for Architecture (CD)
14. Enterprise Architecture for Transformative Research and Collaboration (CD)
15. EC Test Enterprise Governance: An Agile Approach (Test Governance)
A Call for Involvement!
A talk about citation and reproducibility in software, presented at the HSF (High Energy Physics Software Foundation) meeting at SDSC, San Diego, CA, USA, 23 January 2017
Based on citation work done by the FORCE11 Software Citation Working Group as well as recent reproducibility discussions, blogs, and papers
Software Citation: Principles, Implementation, and ImpactDaniel S. Katz
A talk about Software Citation Principles for the 3:am conference (Bucharest, Romania, 28 September 2016), as developed by Arfon M. Smith, Daniel S. Katz, Kyle E. Niemeyer, and the FORCE11 Software Citation Working Group
Big Data Analytics of Software Ecosystem Health: Presentation during INFORTECH Scientific Day (23 May 2018) by Professor Tom Mens. The talk reports on ongoing research of the Software Engineering Lab of the University of Mons (UMONS) on health aspects of evolving software ecosystems. This research was conducted in collaboration with postdoctoral researchers Alexandre Decan and Eleni Constantinou, as well as the external partners of two ongoing research projects: SECOHealth (https://secohealth.github.io) and the Excellence of Science research project SECO-ASSIST (https://secoassist.github.io).
Parsl: Pervasive Parallel Programming in PythonDaniel S. Katz
a seminar presented at the School of Computer Science at the University of St Andrews 18 October 2019 (see https://blogs.cs.st-andrews.ac.uk/csblog/2019/09/25/daniel-katz-parsl/)
A talk about "Conceptualizing a US Research Software Sustainability Institute (URSSI)" presented at the Toward a New Computational Fluid Dynamics Software Infrastructure (CFDSI, https://www.colorado.edu/events/cfdsi/) workshop in Boulder, CO, 16 May 2018.
A brief status of software citation work presented at AAS splinter meeting on implementing the FORCE11 Software Citation Principles in Astronomy (2018-01-11)
Looking at Software Sustainability and Productivity Challenges from NSFDaniel S. Katz
A lightning talk by Daniel S. Katz and Rajiv Ramnath (NSF) at CSESSP workshop - https://www.nitrd.gov/csessp/
Based on a white paper, at http://arxiv.org/abs/1508.03348
Scientific research: What Anna Karenina teaches us about useful negative resultsDaniel S. Katz
a panel talk for the 1st Workshop on E-science ReseaRch leading tO negative Results (ERROR), held in conjunction with the 11th eScience conference on 3 September 2015 in Munich, Germany
Panel: Our Scholarly Recognition System Doesn’t Still WorkDaniel S. Katz
A panel at the 2015 Science of Team Science (SciTS) Conference
Organizers: Daniel S. Katz (U. of Chicago & Argonne National Laboratory), Amy Brand (Digital Science), Melissa Haendel (Oregon Health & Science University), Holly J. Falk-Krzesinski (Elsevier)
Panelists: Robin Champieux (Oregon Health & Science University) Holly Falk-Krzesinski (Elsevier)Daniel S. Katz (U. of Chicago & Argonne National Laboratory)Philippa Saunders (University of Edinburgh)
Abstract: http://bit.ly/scholarly-recognition
US University Research Funding, Peer Reviews, and MetricsDaniel S. Katz
My part of the "Digital Science Webinar: Articulating Research Impact – Strategies from Around the Globe" (http://www.digital-science.com/events/digital-science-webinar-articulating-research-impact-strategies-from-around-the-globe/)
Daniel S. Katz will discuss how reviewers at the National Science Foundation (USA) consider the “intellectual merit” and “broader impacts” criteria for funding and in particular how metrics might help applicants understand their impacts in these areas.Dan will also talk about how reviewers might use qualitative and quantitative altmetrics data to inform their peer reviews for grant applications. He will address many of the salient questions around this use of metrics, for example, do reviewers take metrics seriously and what types of metrics are of most value to them?
Swift Parallel Scripting for High-Performance WorkflowDaniel S. Katz
The Swift scripting language was created to provide a simple, compact way to write parallel scripts that run many copies of ordinary programs concurrently in various workflow patterns, reducing the need for complex parallel programming or arcane scripting to achieve this common high-level task. The result was a highly portable programming model based on implicitly parallel functional dataflow. The same Swift script runs on multi-core computers, clusters, grids, clouds, and supercomputers, and is thus a useful tool for moving workflow computations from laptop to distributed and/or high performance systems.
Swift has proven to be very general, and is in use in domains ranging from earth systems to bioinformatics to molecular modeling. It’s more recently been adapted to serve as a programming model for much finer-grain in-memory workflow on extreme scale systems, where it can perform task rates in the millions to billion-per-second.
In this talk, we describe the state of Swift’s implementation, present several Swift applications, and discuss ideas for of the future evolution of the programming model on which it’s based.
A Method to Select e-Infrastructure Components to SustainDaniel S. Katz
This is a talk presented at International Symposium on Grids and Clouds (ISGC), Taipei, Taiwan, March 20, 2015.
Abstract:
Reusable infrastructure (systems created by one or more people and intended to be used by other people) has become essential for many types of research over the last century, from microscopes to telescopes, and from sequencers to colliders. Over the past few decades, much research infrastructure has become digital, and many new digital systems have been developed. Here we discuss e-Research infrastructure (also called cyberinfrastructure), which has been defined by Craig Stewart as consisting of “... computing systems, data storage systems, advanced instruments and data repositories, visualization environments, and people, all linked together by software and high performance networks to improve research productivity and enable breakthroughs not otherwise possible.” While the research infrastructure as a whole is important, it is useful to consider infrastructure elements as well, as they comprise the overall infrastructure. Each element has a technical context (which allows one to ask questions about its architecture, such as: How does it fit into the overall infrastructure? How does it interact with other infrastructure elements?), a social context (which allows one to ask questions about its developers, such as: Who has developed the element?, and it users, such as: Who uses the element?, and its purpose, such as: What is the intended use of the element?), and a political context (which allows one to ask questions about its funders, such as: Who funds the development and maintenance?, and about its political scope, such as: Is the element national? International?). Understanding how a particular infrastructure element can be created and sustained requires answering two pairs of questions: What resources are needed to create it, and how can those resources be assembled and applied? What resources are needed to sustain it, and how can those resources be assembled and applied? In this paper, we focus on the second half of the two questions, since the amount and type of needed resources vary with the specific element being discussed. We believe elements of e-Research infrastructure can be placed in a three-dimensional space, consisting of temporal duration, spatial extent, and purpose. Note that the number of users of a given element should be larger the farther the element is from the origin in any direction, as should the cost. These two elements (number of users and cost) can be generically called ‘scale’ in this context. Alternatively, we can attempt to map impact, rather than usage, as an element of scale. In either case, scale is thus a metric of the space, though it is not orthogonal to any of the three axes. This talk with discuss how placing potential elements in this space allows decisions to be made on which elements should be pursued.
Multi-component Modeling with Swift at Extreme ScaleDaniel S. Katz
Presentation given at Supercomputing Frontiers 2015 (http://supercomputingfrontiers2015.com), Singapore, March 17, 2015.
Abstract: As both computing systems and science and engineering applications both grow larger and more complex, new challenges arise in abstractly understanding the applications on the systems, productively programming them, and using them to solve real problems. We believe that a large portion of these challenges can be addressed through an appreciation of the hierarchies in such systems, exposed to the user by means of orchestration. The Swift language’s ability to implicitly include parallelism and its highly scalable runtime system allow us to define, express, and efficiently execute applications composed of large-scale parallel components with a variety of connective elements, such as concurrent computational simulations, mathematically oriented data analysis frameworks, and computational simulations with in-situ data analysis.
The Application Fault Tolerance (AFT) portion of the Jet Propulsion Laboratory-led Remote Exploration and Experimentation (REE) final review, May 2001, with references to REE-produced AFT papers added after the review (last three slides)
Some thoughts on how research and infrastructure software are supported by NSF (and possibly other agencies), for the "What can academia learn from open source?" Academia Town Hall - https://ti.to/github-events/academia-town-hall-
Metrics & Citation for Software (and Data)Daniel S. Katz
A talk about why metrics and citation for software (and data) are important to NSF and the science & engineering community, and what a number of projects are trying to do to improve the situation. Presented at "Workshop on Supporting Scientific Discovery through Norms and Practices for Software and Data Citation and Attribution", Washington, DC, 29 Jan 2015
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
The Evolution of Science Education PraxiLabs’ Vision- Presentation (2).pdfmediapraxi
The rise of virtual labs has been a key tool in universities and schools, enhancing active learning and student engagement.
💥 Let’s dive into the future of science and shed light on PraxiLabs’ crucial role in transforming this field!
Travis Hills' Endeavors in Minnesota: Fostering Environmental and Economic Pr...Travis Hills MN
Travis Hills of Minnesota developed a method to convert waste into high-value dry fertilizer, significantly enriching soil quality. By providing farmers with a valuable resource derived from waste, Travis Hills helps enhance farm profitability while promoting environmental stewardship. Travis Hills' sustainable practices lead to cost savings and increased revenue for farmers by improving resource efficiency and reducing waste.
BREEDING METHODS FOR DISEASE RESISTANCE.pptxRASHMI M G
Plant breeding for disease resistance is a strategy to reduce crop losses caused by disease. Plants have an innate immune system that allows them to recognize pathogens and provide resistance. However, breeding for long-lasting resistance often involves combining multiple resistance genes
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
The use of Nauplii and metanauplii artemia in aquaculture (brine shrimp).pptxMAGOTI ERNEST
Although Artemia has been known to man for centuries, its use as a food for the culture of larval organisms apparently began only in the 1930s, when several investigators found that it made an excellent food for newly hatched fish larvae (Litvinenko et al., 2023). As aquaculture developed in the 1960s and ‘70s, the use of Artemia also became more widespread, due both to its convenience and to its nutritional value for larval organisms (Arenas-Pardo et al., 2024). The fact that Artemia dormant cysts can be stored for long periods in cans, and then used as an off-the-shelf food requiring only 24 h of incubation makes them the most convenient, least labor-intensive, live food available for aquaculture (Sorgeloos & Roubach, 2021). The nutritional value of Artemia, especially for marine organisms, is not constant, but varies both geographically and temporally. During the last decade, however, both the causes of Artemia nutritional variability and methods to improve poorquality Artemia have been identified (Loufi et al., 2024).
Brine shrimp (Artemia spp.) are used in marine aquaculture worldwide. Annually, more than 2,000 metric tons of dry cysts are used for cultivation of fish, crustacean, and shellfish larva. Brine shrimp are important to aquaculture because newly hatched brine shrimp nauplii (larvae) provide a food source for many fish fry (Mozanzadeh et al., 2021). Culture and harvesting of brine shrimp eggs represents another aspect of the aquaculture industry. Nauplii and metanauplii of Artemia, commonly known as brine shrimp, play a crucial role in aquaculture due to their nutritional value and suitability as live feed for many aquatic species, particularly in larval stages (Sorgeloos & Roubach, 2021).
Nucleophilic Addition of carbonyl compounds.pptxSSR02
Nucleophilic addition is the most important reaction of carbonyls. Not just aldehydes and ketones, but also carboxylic acid derivatives in general.
Carbonyls undergo addition reactions with a large range of nucleophiles.
Comparing the relative basicity of the nucleophile and the product is extremely helpful in determining how reversible the addition reaction is. Reactions with Grignards and hydrides are irreversible. Reactions with weak bases like halides and carboxylates generally don’t happen.
Electronic effects (inductive effects, electron donation) have a large impact on reactivity.
Large groups adjacent to the carbonyl will slow the rate of reaction.
Neutral nucleophiles can also add to carbonyls, although their additions are generally slower and more reversible. Acid catalysis is sometimes employed to increase the rate of addition.
This presentation explores a brief idea about the structural and functional attributes of nucleotides, the structure and function of genetic materials along with the impact of UV rays and pH upon them.
Professional air quality monitoring systems provide immediate, on-site data for analysis, compliance, and decision-making.
Monitor common gases, weather parameters, particulates.
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
1. Research Software Sustainability
NITRD HEC IWG, 16 January 2020
Daniel S. Katz
(d.katz@ieee.org, http://danielskatz.org, @danielskatz)
Assistant Director for Scientific
Software & Applications
Research Associate Professor,
CS, ECE, iSchool
2. Why do we care about research software?
• NSF
• 1995-2016: 18,592 awards totalling $9.6 billion with project abstracts that
topically include “software”
• ~20% of the overall NSF research budget
• DOE
• Of 3 ECP areas, most of 2 (application development & software)
technology are research software
• According to Paul Messina in 2017,
“ECP is a 7-year project with a
cost range of $3.5B–$5.7B”
Collected from http://www.dia2.org in 2017
3. Why do we care about research software?
• 40 papers in Nature (Jan-Mar 2016)
• 32 explicitly mentioned software
• Average of 6.5 software tools/paper
• Most of which were research software
• Top 100-cited papers:
• 6 of top 13 are software papers
• “… the vast majority describe
experimental methods or software that
have become essential in their fields.”
Nangia and Katz; 10.1109/eScience.2017.78 “Top 100-cited papers of all time,” Nature, 2014 10.1038/514550a
4. Why do we care about research software?
• Surveys of UK academics at Russell Group Universities (2014)
and members of (US) National Postdoctoral Research Association
(2017):
• I use research software: 92% / 95% (UK/US)
• My research would not be possible without software: 67% / 63%
• My research would be possible but harder: 21% / 31%
• I develop my own software: 56% / 28%
S. Hettrick, “It's impossible to conduct research without software, say 7 out of 10 UK researchers,”
Software Sustainaiblity Institute, 2014. Available at: https://www.software.ac.uk/blog/2016-09-12-its-
impossible-conduct-research-without-software-say-7-out-10-uk-researchers
S.J. Hettrick, M. Antonioletti, L. Carr, N. Chue Hong, S. Crouch, D. De Roure, et al, “UK Research Software
Survey 2014”, Zenodo, 2014. 10.5281/zenodo.14809
U. Nangia and D. S. Katz, “Track 1 Paper: Surveying the U.S. National Postdoctoral Association Regarding Software Use and
Training in Research,” WSSSPE5.1, 2017. 10.6084/m9.figshare.5328442.v1
5. Science of research software organizations
• Software Sustainability Institute (SSI)
• In third period of funding
• Now funded by all UK research councils
• Better Scientific Software (BSSw)
• Clearinghouse to gather, discuss, and disseminate experiences, techniques,
tools, and other resources to improve developer productivity and software
sustainability
• DOE funded
• United States Research Software Sustainability Institute (URSSI)
• Conceptualization project under NSF funding
• Institute proposal planned in 2020
• Interest from private foundations
• Research Software Alliance (ReSA)
• Intended to coordinate the above & others internationally
6. Open source & software growth
• 2001: 208K SourceForge users
• 2017: 20M GitHub users
• 2019: 37M GitHub users
• 1998: 180K downloads of Netscape (app) in 2 weeks
• 2017: 21M downloads of lodash (javascript library) in 2 weeks
• 2018 survey of scientist-developers found that 82% of respondents felt
that they were spending spending “more time” or “much more time”
developing software than they did 10 years ago
Credit: “Rebuilding the cathedral” @nayafia for Strange Loop 2017
Pinto at al., “How do scientists develop scientific software? an external replication,” 10.1109/SANER.2018.8330263
7. Software in research cycle
Create
Hypothesis
Acquire
Resources (e.g.,
Funding,
Software, Data)
Perform
Research (Build
Software &
Data)
Publish
Results (e.g.,
Paper, Book,
Software, Data)
Gain
Recognition
Knowledge
Infrastructure
Research
8. Research software vs. infrastructure software
• Some research software is intended just for research
• Funded by many agencies, sometimes explicitly, often implicitly
• Intended for immediate use by developer
• Maybe archived for future use and reproducibility
• And probably dependent on infrastructure software
• Other research software is intended as infrastructure
• To be shared
• Funded by some agencies, almost always explicitly
• Intended for use by community
• Appreciation and reward easier because of sharing
• Software intended for research can be turned into infrastructure software
• Requires making a conscious choice
• Has consequences, both positive and negative
9. Who starts new infrastructure software projects?
• Tool makers
• To make something
useful to others
• Then options:
• Accept contributions?
and if so:
a. Broaden focus?
• Bring together other (related) packages
b. Broaden governance?
• Collaborate with other developers
10. Parsl: Interactive parallel programming in Python
Apps define opportunities for parallelism
• Python apps call Python functions
• Bash apps call external
applications
Apps return “futures”: a proxy for a result
that might not yet be available
Apps run concurrently respecting data
dependencies. Natural parallel
programming!
Parsl scripts are independent of where
they run. Write once run anywhere!
pip install parsl
https://parsl-project.org/
11. Parsl project summary
• Based on improving Swift workflow system/language & 10+ years of CS/infrastructure
research
• Initially funded by NSF, $3m over 3 years (stretching to 4)
• 2.5 core developer FTEs, PI, co-PIs, chemistry & education application developers,
undergraduate & graduate students
• Open source, intended as open community, including library of reusable workflows
• Interesting milestones
• First outside user
• First outside user who didn’t contact us
• First outside contributor
• First outside contributor who didn’t contact us
• Some success with purely external contributions to code, more success with
collaborating projects (e.g., LSST-DESC, Thain group @ ND), some providing funding
12. Who starts new research software projects?
• User/Developer
• To scratch their own itch
• Then options:
1. Keep it private
2. Share it
3. Accept contributions?
and if so:
a. Broaden focus?
• Bring together other (related) packages
b. Broaden governance?
• Collaborate with other developers
13. FDTD electromagnetics
• My PhD dissertation was “Boundary treatments applied to
the FD-TD method for solving problems of
electromagnetic wave propagation” (1994)
• Adding methods to treat curved materials and infinite
space to my advisor/lab’s time-marching code that solved
EM problems on a finite cartesian grid
• Code originally written in late 1970s, FORTRAN77, no
subroutines
• While I was working on it: heavily modified, better
engineered, ported to various languages, vector
computing, parallel computing
• But never shared – was viewed as part of the lab’s IP
• Algorithm was shared, leading others to redevelop code,
now many versions including open source and commercial
Top figure: J. B. Schneider, Understanding the Finite-Difference Time-Domain Method, www.eecs.wsu.edu/~schneidj/ufdtd, 2010.
Time steps of a gaussian pulse, travelling on a
microstrip, showing coupling to a neighboring
strip, and crosstalk to a crossing strip. Colors
showing currents are relative to the peak
current on that strip. Pulse: rise time = 70 ps,
freq. ≈ 0 to 30 GHz. Grid dimensions = 282 ´
362 ´ 102 cells. Cell size = 1 mm3.
Bottom images produced at U of Colorado’s Comp. EM Lab. By Matt Larson using SGI’s LC FDTD code
14. Project stages
S. P. Benthal, Software Incubator Workshop: A Synthesis, lhttp://urssi.us/blog/2019/02/25/software-incubator-workshop-a-synthesis/
15. Changing stages
• At each point/stage, decide consciously to go forward
• Think about methods, goals, and consequences
• What resources are available to help
• What (type) of work will be needed?
• Are the right skills available?
• What are the incentives?
• How will success be
measured?
• How will the institution(s)
support this?
J. Leng , M. Shoura, T. C. B. McLeish, A. N. Real, et al. “Securing the future of research computing in the biosciences,”
PLoS Comput Biol 15(5): e1006958, 2019. https://doi.org/10.1371/journal.pcbi.1006958
16. Software collapse
• Software stops working eventually if is not actively maintained
• Structure of computational science software stacks:
1. Project-specific software (developed by researchers): software to do a computation
using building blocks from the lower levels: scripts, workflows, computational
notebooks, small special-purpose libraries & utilities
2. Discipline-specific software (developed by developers & researchers): tools &
libraries that implement disciplinary models & methods
3. Scientific infrastructure (developed by developers): libraries & utilities used for
research in many disciplines
4. Non-scientific infrastructure (developed by developers): operating systems,
compilers, and support code for I/O, user interfaces, etc.
• Software builds & depends on software in all layers below it; any change
below may cause collapse
K. Hinsen, “Dealing With Software Collapse,” 2019. https://doi.org/10.1109/MCSE.2019.2900945
17. Software sustainability
• Software sustainability ≡ the capacity of the software to endure
• Will the software will continue to be available in the future, on new platforms,
meeting new needs?
• Software development and maintenance requires human effort
• Human effort ⇆ $
• All human effort works (community open source)
• All $ (salary) works (commercial software, grant funded projects)
• Combined is hard: effort ≠ $; humans are not purely rational
18. Example/Problem 1: OpenSSL
• 1998: UK group built internet encryption tools: OpenSSL
• 2011: Heartbleed bug introduced
• 2014: 2/3 of web sites rely on OpenSSL
• One full-time developer: Steven Hensen, barely supported by
OpenSSL Software Foundation (OSF)
• Private, for-profit company
• 2014: Heartbleed bug discovered
• OpenSSL bug fixed
• OSF requests donations
• $9,000 given initially
• Further campaign led to support for 4 developers for 3 years
• And now what?
Credit: Nadia Eghbal, “Roads and Bridges,” 2016
19. Example/Problem 2: Bus factor
• NumFOCUS: Umbrella non-profit
to support scientific software
• NumFOCUS sustainability
summit annually since 2017
• 2017 bus factor survey
• Bimodal, ~half 1-2, ~half 4-6
• One project’s story: developer
support, backlog, students
• Wider open source community
• Two-thirds of popular projects: bus
factor of 1 or 2
G. Avelino, M. T. Valente, A. Hora, "What is the Truck Factor of popular GitHub applications? A first
assessment,” PeerJ Preprints 5:e1233v3, 2017. https://doi.org/10.7287/peerj.preprints.1233v3
O e
J al
N m Ma l lib
J m
Ca e a B keh A
S a
20. Example/Problem 3: Faculty recognition
• Young faculty member, very involved in open source,
open science, reproducibility
• In recent pre-promotion case meeting with chair, told:
• Committee won’t count software efforts or papers
• They’re not “traditional” journal papers
• Visibility and recognition for open science & reproducibility are
ok …
• but official h-index is more important and not high enough
22. Research software
• Software developed and used for the purpose of research: to generate, process,
analyze results within the scholarly process
• Increasingly essential in the research process
• But
• Software will collapse if not maintained
• Software bugs are found, new features are needed, new platforms arise
• Software development and maintenance is human-intensive
• Much software developed specifically for research, by researchers
• Researchers know their disciplines, but often not software best practices
• Researchers are not rewarded for software development and maintenance in
academia
• Developers don’t match the diversity of overall society or of user communities
23. Max Planck
• Eine neue wissenschaftliche Wahrheit pflegt sich nicht in der
Weise durchzusetzen, daß ihre Gegner überzeugt werden und
sich als belehrt erklären, sondern vielmehr dadurch, daß ihre
Gegner allmählich aussterben und daß die heranwachsende
Generation von vornherein mit der Wahrheit vertraut gemacht ist
• A new scientific truth does not triumph by convincing its
opponents and making them see the light, but rather because its
opponents eventually die, and a new generation grows up that is
familiar with it
• Or: science advances one funeral at a time
• My version: culture of science advances one funeral at a time
24. • Enough bad news
• What can we do?
• Wait
• cf Planck
• Or act
25. 12 scientific software challenges
• Incentives, citation/credit models, and metrics
• Career paths
• Training and education
• Software engineering
• Portability
• Intellectual property
• Publication and peer review
• Software communities and sociology
• Sustainability and funding models
• Software dissemination, catalogs, search, and review
• Multi-disciplinary science
• Reproducibility
All are tied together
https://www.slideshare.net/danielskatz/scientific-software-challenges-and-community-responses
26. Credit for software: What to measure?
1.Developer of open source physics simulation
• Possible metrics
• How many downloads?
• How many contributors?
• How many uses?
• How many papers cite it?
• How many papers that cite it are cited?
2. Developer of open source math library
• Possible metrics are similar, but citations are less likely
• May not be able to measure downloads
• It’s part of a distribution
• It’s pre-installed (and optimized) on an HPC system
• It’s part of a cloud image
• It’s a service
Easeof
measurement
Valueof
measurement
im
pact
27. Directly measuring software
• Downloads
• From web/repository logs/stats
• Installations/Builds
• Add “phone home” mechanism at build level
• As done by Homebrew package manager for OS X
• e.g., add ‘curl URL’ in makefile
• Uses/Runs
• Track centrally; add “phone home” mechanism at run level
• Per app, e.g. add ‘curl URL’ in code, send local log to server
• For a set of apps, e.g., sempervirens project, Debian popularity contest
• Track locally; ask user to report
• e.g., duecredit project, or via frameworks like Galaxy
28. Measuring software impact
• Citations
• Because citation system was created for papers/books
• Need to jam software into current citation system
• Altmetrics
• Not citations, but other structured measures of discussion (tweets, blogs, etc.)
• ImpactStory
• Measures research impact: reads, citations, tweets, etc.
• Depsy (roughly ImpactStory specifically for software)
• Measures software impact: downloads, software reuse (if one package is forked into another
package), citations, tweets, etc.
• Libraries.io
• Counts software dependencies
29. Software citation today
• Software and other digital resources currently appear in publications in
very inconsistent ways
• Howison & Bullard: random sample 90 articles in biology literature -> 7
different types of software mentions
• Studies on data and facility citation -> similar results
J. Howison and J. Bullard. Software in the scientific literature: Problems with seeing, finding, and using software mentioned in the
biology literature. Journal of the Association for Information Science and Technology, 2015. https://doi.org/10.1002/asi.23538
30. Journal of Open Source Software (JOSS)
• A developer friendly journal for research software packages
• “If you've already licensed your code and have good documentation then we expect that it
should take less than an hour to prepare and submit your paper”
• Everything is open:
• Submitted/published paper: http://joss.theoj.org
• Code itself: where is up to the author(s)
• Reviews & process: https://github.com/openjournals/joss-reviews
• Code for the journal itself: https://github.com/openjournals/joss
• JOSS papers archived, have DOIs, increasing indexed
• First paper submitted 4 May 2016
• 31 May 2017: 111 accepted papers, 56 under review and pre-review
• 15 January 2020: 803 accepted papers, 115 under review and pre-review
• Current publication rate: ~400 papers/year
• Editors: 1 editor-in-chief and 11 editors at launch;
1 EiC, 5 associate EiCs, 29 topic editors, 11 emeritus editors today
31. Software citation principles
• FORCE11 Software Citation group started July 2015
• WSSSPE3 Credit & Citation working group joined September 2015
• ~55 members (researchers, developers, publishers, repositories, librarians)
• Reviewed existing community practices & developed use cases
• Drafted & published software citation principles
• Started with data citation principles, updated based on software use cases and related
work, updated based working group discussions, community feedback, workshop
• Smith AM, Katz DS, Niemeyer KE, FORCE11 Software Citation Working Group.(2016)
Software Citation Principles. PeerJ Computer Science 2:e86. DOI: 10.7717/peerj-cs.86
• Principles: importance, credit and attribution, unique identification, persistence,
accessibility, specificity
• Software Citation Working Group ended April 2017
32. Software citation implementation
• FORCE11 Software Citation Implementation Working Group in progress, started May 2017
• Co-chairs: Neil Chue Hong, Martin Fenner, Daniel S. Katz
• Goal is implementing software citation
• Working with institutions, publishers, technology and service providers funders, researchers, etc.
• Lots of good work being done, and good coordination of ongoing activities
• Metadata standards and translation (DataCite Schema 4.1, CodeMeta, citation.cff), being
aligned with schema.org
• Open source archiving and identification (Software Heritage) developing
• Good work and initial acceptance in communities (astronomy, Earth science, math, …)
• Published Software Citation Checklist for Authors v0.9 document
• Published Software Citation Checklist for Developers v0.9 document
• Software Citation Checklist for Reviewers document under review
• Repositories task force developing good/best practices for registries and repositories
• Journals task force started in Jan 2020
33. 12 scientific software challenges
• Incentives, citation/credit models, and metrics
• Career paths
• Training and education
• Software engineering
• Portability
• Intellectual property
• Publication and peer review
• Software communities and sociology
• Sustainability and funding models
• Software dissemination, catalogs, search, and review
• Multi-disciplinary science
• Reproducibility
All are tied together
https://www.slideshare.net/danielskatz/scientific-software-challenges-and-community-responses
34. Challenge: better career paths?
• Career paths for software developers in universities unclear
• Should we give up in favor of national labs &
government intramural researchers & industry?
• More financial rewards, cohorts (others with similar
problems and solutions) and promotion opportunities
• For researchers, published software or
software papers make software a valued
output similar to publications
• University centers, e.g. NCSA, SDSC,
TACC, give programmers a home & critical mass for career paths
• Moore/Sloan Data Science program created new structure across
universities
• Research Software Engineers (RSEs) …
35. Who are Research Software Engineers?
• Not independent researchers
• No personal research agenda
• Facilitative, supportive, and collaborative
• Part of the academic community
• With professional IT skills
• Deep engagement with research groups
• Understand, study, and be part of group research activities
• Can read and understand the papers
• Sustainable and long term
• Institutional memory
• Continuity, stability, maintenance
• But in most institutions
• Without a formal home in academia, or a career path
Credit: James Hetherington, “Computational Science as a Service,” http://github-pages.ucl.ac.uk/rsd-talks/rsd/experiences-reveal.html
36. The Story of RSEs
• April 2012: Idea & name at SSI Collaborations Workshop
• September 2012: University College London group founded
• More groups: Manchester (2014); Sheffield, Southampton, and Cambridge (2015)
• January 2016: EPSRC awards first RSE Fellowships
• RSE Conferences in the UK
• September 2016: First RSE conference, 202 attendees, 14 countries
• September 2017: Second RSE conference, 224 attendees
• September 2018: Third RSE conference, 340 attendees
• September 2019: Fourth UK RSE conference, 360 attendees
• Society of Research Software Engineering – formed 2019
• Independent organization for RSEs: international, membership fee, voting rights
• Other countries at various stages of development
• Germany, Netherlands, Nordic, Canada, US, South Africa, Australia, …
• First conferences in Germany and Netherlands occurred in 2019
• 250 signed up on US RSE (as of December 2019)
Credit: James Hetherington, “Computational Science as a Service,” http://github-pages.ucl.ac.uk/rsd-talks/rsd/experiences-reveal.html
37. Promotion and evaluation
• Guidelines for promotion and evaluation important
• Say what’s valued; shape activities people undertake
• Promotion guidelines written by senior people, how can they be
changed?
• We can influence these processes when we participate in these
evaluations
• We can provide templates and guidelines for recognizing
software contributions and encourage respected organizations to
adopt them
• Multiple groups working in this space
38. Promotion and evaluation are not fixed
• National Academies (1994): “Academic Careers for
Experimental Computer Scientists and Engineers”
• Experimental artifacts are important in CS, should be part of
evaluation
• Intended to provide a reference point for change
• Has been quoted in many tenure recommendation letters
• NSF 2013 biosketch change: products, not
publications
• Acknowledges software contributions as a primary research
product
• Intended to signal to universities that they should do the same
https://www.nap.edu/read/2236/https://www.nsf.gov/pubs/policydocs/pappguide/nsf13001/gpg_index.jsp
39. Potential solutions
• Convince governments and funders of importance of software
(and sustained funding for some of it, including maintenance)
• via Research Software Alliance (ReSA) and others
• Encourage use of software citation to aid developers
• via FORCE11 Software Citation Implementation Working Group
• Build better career paths for developers
• via Research Software Engineer (RSE) movement (https://rse.ac.uk and
http://us-rse.org), departments, labs
• Develop and use software best practices
• via Project Carpentry, Incubators (e.g. ESIP, Apache)
• Join groups working on these
• SSI, URSSI, NumFOCUS, CS&S
40. Credits
• Thanks to Arfon Smith and Kyle Niemeyer for
co-leadership in FORCE11 Software Citation WG
• And Neil Chue Hong & Martin Fenner for
co-leadership in FORCE11 Software Citation Implementation WG
• And colleagues Gabrielle Allen, C. Titus Brown, Kyle Chard, Ian
Foster, Melissa Haendel, Christie Koehler, Bill Miller, Rajiv
Ramnath
• And to the BSSw project (http://bssw.io) for a fellowship to pursue
some parts of the citation work
• More of my thinking
• Blog: http://danielskatzblog.wordpress.com
• Tweets: @danielskatz