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All animals have to respond to immediate threats in order to survive. In non-human animals, a diversity of sophisticated behaviours has been observed, but research in humans is hampered by ethical considerations. Here, we present a novel immersive VR toolkit for the Unity engine that allows assessing threat-related behaviour in single, semi-interactive, and semi-realistic threat encounters. The toolkit contains a suite of fully modelled naturalistic environments, interactive objects, animated threats, and scripted systems. These are arranged together by the researcher as a means of creating an experimental manipulation, to form a series of independent "episodes" in immersive VR. Several specifically designed tools aid the design of these episodes, including a system to allow for pre-sequencing the movement plans of animal threats. Episodes can be built with the assets included in the toolkit, but also easily extended with custom scripts, threats, and environments if required. During the experiments, the software stores behavioural, movement, and eye tracking data. With this software, we aim to facilitate the use of immersive VR in human threat avoidance research and thus to close a gap in the understanding of human behaviour under threat. [ABSTRACT FROM AUTHOR]

Software plays a fundamental role in research as a tool, an output, or even as an object of study. This special issue on software citation, indexing, and discoverability brings together five papers examining different aspects of how the use of software is recorded and made available to others. It describes new work on datasets that enable large-scale analysis of the evolution of software usage and citation, that presents evidence of increased citation rates when software artifacts are released, that provides guidance for registries and repositories to support software citation and findability, and that shows there are still barriers to improving and formalising software citation and publication practice. As the use of software increases further, driven by modern research methods, addressing the barriers to software citation and discoverability will encourage greater sharing and reuse of software, in turn enabling research progress. [ABSTRACT FROM AUTHOR]

Research software is continuously developed to facilitate progress and innovation in the medical field. Over time, numerous research software programs have been created, making it challenging to keep abreast of what is available. This work aims to evaluate the most frequently utilized software by the computer-assisted intervention (CAI) research community. The software assessments encompass a range of criteria, including load time, stress load, multi-tasking, extensibility and range of functionalities, user-friendliness, documentation, and technical support. A total of eight software programs were selected: 3D Slicer, Elastix, ITK-SNAP, MedInria, MeVisLab, MIPAV, and Seg3D. While none of the software was found to be perfect on all evaluation criteria, 3D Slicer and ITK-SNAP emerged with the highest rankings overall. These two software programs could frequently complement each other, as 3D Slicer has a broad and customizable range of features, while ITK-SNAP excels at performing fundamental tasks in an efficient manner. Nonetheless, each software had distinctive features that may better fit the requirements of certain research projects. This review provides valuable information to CAI researchers seeking the best-suited software to support their projects. The evaluation also offers insights for the software development teams, as it highlights areas where the software can be improved. [ABSTRACT FROM AUTHOR]

Social media research software has come to play increasingly important roles in processes of knowledge production. While epistemological, logistical, legal, and ethical concerns put the spotlight on the software tools researchers are relying on, little attention is paid to the role of the 'toolmaker' beyond a vague idea of the 'power' wielded by those who design, develop, and maintain these technical artifacts. This paper seeks to address this role, both conceptually and with attention to practical concerns, as a form of hybrid and relational authorship. We thereby shift the focus from tool to tool-making, from artifact to practice, in an attempt to produce a different kind of 'unblackboxing' of tools than the somewhat overused tropes of open source code or open data. Our contribution proceeds in three steps. We first address tools and tool-making from a theoretical perspective, suggesting that their epistemological orientation reaches more deeply into the networks of research practice than words like 'bias' admit and proposing to consider the specific kind of hybrid authorship that emerges in this context. Calling on our own experiences as toolmakers, we then reflect on a cluster of issues where this authorial function becomes particularly visible. Here, we examine how motivations and commitments orient what a piece of software does and how it does it and discuss tool-making from the perspectives of co-development, maintenance and care, and ethics by design. We conclude by arguing that the most pressing concerns for tool-making lie in institutional arrangements that are crucial for the life of research software. [ABSTRACT FROM AUTHOR]

Background. Open Science seeks to render research outputs visible, accessible, reusable. The Open Science framework is currently evolving vigorously due, among others reasons, to the UNESCO Open Science Recommendation adopted in November 2021. In this context, it is relevant to better visualize and describe the relationships that hold among the direct protagonists of this changing landscape: research teams and their research outputs, namely: articles, software and data, as their comprehension will certainly contribute to foster better Open Science practices. Method. In this work we review and describe, through the information collected in a large number of bibliographic references, the current changing trends involving some essential, defining, characteristics and behaviors of the main components of the scientific production, namely, research teams and three kinds of research outputs they produce in many scientific areas. This comparative study is based, among others, in our recent work on the evolving concepts of research software, research data in the context of Open Science. Results. In this work we observe and document some key features in this evolving landscape such as the changing and extended roles of research team members; the need to develop a new citing and referencing culture for articles, but specially for research software and data; the rising relevance of open access (to publications, software, data) policies all over the world; the existence of some barriers and difficulties like the regulations concerning academic research close to industry, or other technological applications; the need to develop standards for the “right to be forgotten”; the need to consider the impact of Open Science costs for less favored communities, countries, institutions. . . Conclusions. This calls for the urgent need to observe and depict further this changing Open Science ecosystem, and to propose –as we have partially attempted in this work– new concepts to analyze this context as well as to contribute to ongoing research-on-research and to improve the implementation of Open Science practices, in order to foster better ways towards a sound, inclusive and fairer Open Science landscape. [ABSTRACT FROM AUTHOR]

Software plays a fundamental role in research as a tool, an output, or even as an object of study. This special issue on software citation, indexing, and discoverability brings together five papers examining different aspects of how the use of software is recorded and made available to others. It describes new work on datasets that enable large-scale analysis of the evolution of software usage and citation, that presents evidence of increased citation rates when software artifacts are released, that provides guidance for registries and repositories to support software citation and findability, and that shows there are still barriers to improving and formalising software citation and publication practice. As the use of software increases further, driven by modern research methods, addressing the barriers to software citation and discoverability will encourage greater sharing and reuse of software, in turn enabling research progress.

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Copyright of Publizistik: Vierteljahreshefte für Kommunikationsforschung is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Machine learning (ML) and advanced computational methods are powerful tools for processing and deriving value from large data volumes. These methods are being developed and deployed rapidly, but best practices are still evolving regarding code and data standards, leading to irreproducibility of ML-enabled research. In this Practice Paper, we describe our efforts to make a ML-enabled research project to create a global inventory of biodata resources open and reproducible. To contribute to community conversations on evolving norms and expectations, we present our experiences as a practical, real-world case study that includes the implementation details as well as our overall approach and subsequent decisions. Our goal in openly sharing this experience is to provide a concrete example that others may consider as they look to vet, adapt, and adopt similar strategies to make their own work open and reproducible.

Software development has become an integral part of the scholarly ecosystem, spanning all fields and disciplines. To support the sharing and creation of knowledge in line with open science principles, and particularly to enable the reproducibility of research results, it is crucial to make the source code of research software available, allowing for modification, reuse, and distribution. Recognizing the significance of open-source software contributions in academia, the second French Plan for Open Science, announced by the Minister of Higher Education and Research in 2021, introduced a National Award to promote open-source research software. This award serves multiple objectives: firstly, to highlight the software projects and teams that have devoted time and effort to develop outstanding research software, sometimes for decades, and often with little recognition; secondly, to draw attention to the importance of software as a valuable research output and to inspire new generations of researchers to follow and learn from these examples. We present here an in-depth analysis of the design and implementation of this unique initiative. As a national award established explicitly to foster Open Science practices by the French Minister of Research, it faced the intricate challenge of fairly evaluating open research software across all fields, striving for inclusivity across domains, applications, and participants. We provide a comprehensive report on the results of the first edition, which received 129 high-quality submissions. Additionally, we emphasize the impact of this initiative on the open science landscape, promoting software as a valuable research outcome, on par with publications.

Copyright of ABI Technik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Scholia for Software is a project to add software profiling features to Scholia, which is a scholarly profiling service from the Wikimedia ecosystem and integrated with Wikipedia and Wikidata. This document is an adaptation of the funded grant proposal. We are sharing it for several reasons, including research transparency, our wish to encourage the sharing of research proposals for reuse and remixing in general, to assist others specifically in making proposals that would complement our activities, and because sharing this proposal helps us to tell the story of the project to community stakeholders.A "scholarly profiling service" is a tool which assists the user in accessing data on some aspect of scholarship, usually in relation to research. Typical features of such services include returning the biography of academic publications for any given researcher, or providing a list of publications by topic. Scholia already exists as a Wikimedia platform tool built upon Wikidata and capable of serving these functions. This project will additionally add software-related data to Wikidata, develop Scholia's own code, and address some ethical issues in diversity and representation around these activities. The end result will be that Scholia will have the ability to report what software a given researcher has described using in their publications, what software is most used among authors publishing on a given topic or in a given journal, what papers describe projects which use some given software, and what software is most often co-used in projects which use a given software.

Background: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles – Findability, Accessibility, Interoperability, and Reusability – were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS). Aim of review: This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software. Key scientific concepts of review: We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions. [ABSTRACT FROM AUTHOR]

In a recent article and in the context of open science, the FAIR for Research Software (FAIR4RS) working group introduced the FAIR (Findable, Accessible, Interoperable, and Reusable) principles for research software. This ThinkEPI discusses the difference in these principles between research software (created during research) and software in research (used to write, develop, or process scientific work). Some questions are proposed for discussion, such as: Is it open science if it was written, developed, or processed with proprietary software? Should we consider software in research -and not just research software- as another piece of open science? The debate is currently even more relevant due to the recent publication of the first National Open Science Strategy (ENCA) 2023-2027 by the Government of Spain, and its content that includes, among other topics, references to open source and free software. [ABSTRACT FROM AUTHOR]

In academic research virtually every field has increased its use of digital and computational technology, leading to new scientific discoveries, and this trend is likely to continue. Reliable and efficient scholarly research requires researchers to be able to validate and extend previously generated research results. In the digital era, this implies that digital objectsKahn and Wilensky 2006 used in research should be Findable, Accessible, Interoperable and Reusable (FAIR). These objects include (but are not limited to) data, software, models (for example, machine learning), representations of physical objects, virtual research environments, workflows, etc. Leaving any of these digital objects out of the FAIR process may result in a loss of academic rigor and may have severe consequences in the long term for the field, such as a reproducibility crisis. In this extended abstract, we focus on research software as a FAIR digital object (FDO).The FDO framework De Smedt et al. 2020 describes FDOs as being actionable units of knowledge, which can be aggregated, analyzed, and processed by different types of algorithms. Such algorithms must be implemented by software in one form or another. The framework also describes large software stacks supporting FDOs enabling responsible data science and increasing reproducibility. This implies that software is a key ingredient of the FDO framework, and should adhere to the FAIR principles. Software plays multiple roles: it is a DO itself, it is responsible for creating new FDOs (e.g., data) and it helps to make them available to the public (e.g., via repositories and registries). However there is a need to specify in more detail how non-data DOs, in particular software, fit in this framework.Different classes of digital objects have different intrinsic properties and ways to relate to other DOs. This means that while they, in principle, are subject to the high-level FAIR principles, there are also differences depending on their type and properties, requiring an adaptation so FAIR implementations are more aligned to the digital object itself. This holds true in particular to software. Software has intrinsic properties (executability, composite nature, development practices, continuous evolution and versioning, and packaging and distribution) and specific needs that must be considered by the FDO framework. For example, open source software is typically developed in the open on social coding platforms, where releases are distributed through package management systems, unlike data that is typically published in archival repositories. These social coding platforms do not provide long term archiving, permanent identifiers, or metadata, and package management systems, while somewhat better, similarly do not make a commitment to long term archiving, do not use identifiers that fit the scholarly publication system well, and provide metadata that may be missing key elements. The FAIR for research software (FAIR4RS, Chue Hong et al. 2021) working group has dedicated significant effort in building a community consensus around developing FAIR principles that are customized for research software, providing methods for researchers to understand and address these gaps.In this presentation we will highlight the importance of software for the FAIR landscape and why different (but related) FAIR principles are needed for software (vs those originally developed for data). Our goal here is to contribute to building an FDO landscape together, where we consider all different types of digital objects that are essential in today's research, and we are enthusiastic about contributing our expertise on research software in helping shape this landscape.

This article describes the motivation, design, and progress of the Journal of Open Source Software (JOSS). JOSS is a free and open-access journal that publishes articles describing research software. It has the dual goals of improving the quality of the software submitted and providing a mechanism for research software developers to receive credit. While designed to work within the current merit system of science, JOSS addresses the dearth of rewards for key contributions to science made in the form of software. JOSS publishes articles that encapsulate scholarship contained in the software itself, and its rigorous peer review targets the software components: functionality, documentation, tests, continuous integration, and the license. A JOSS article contains an abstract describing the purpose and functionality of the software, references, and a link to the software archive. The article is the entry point of a JOSS submission, which encompasses the full set of software artifacts. Submission and review proceed in the open, on GitHub. Editors, reviewers, and authors work collaboratively and openly. Unlike other journals, JOSS does not reject articles requiring major revision; while not yet accepted, articles remain visible and under review until the authors make adequate changes (or withdraw, if unable to meet requirements). Once an article is accepted, JOSS gives it a digital object identifier (DOI), deposits its metadata in Crossref, and the article can begin collecting citations on indexers like Google Scholar and other services. Authors retain copyright of their JOSS article, releasing it under a Creative Commons Attribution 4.0 International License. In its first year, starting in May 2016, JOSS published 111 articles, with more than 40 additional articles under review. JOSS is a sponsored project of the nonprofit organization NumFOCUS and is an affiliate of the Open Source Initiative (OSI).

Academic research requires careful handling of data plus any means to collect, transform and publish it, activities commonly supported by research software (from scripts to enduser applications). Data Management Plans (DMPs) are nowadays commonly requested by funders as part of good research practices. A DMP describes the data management lifecycle for the data corresponding to a research project, covering activities from collection to publication and preservation. To support and improve transparency, open science, reproducibility (and other *ilities), data needs to be accompanied by the software transforming it. Similar to DMPs, Software Management Plans (SMPs) can help formalize a set of structures and goals ensuring that the software is accessible and reusable in the short, medium and long term. DMPs and SMPs can be presented as text-based documents, guided by a set of questions corresponding to key points related to the lifecycle of either data or software. A step forward for DMPs are the machine-actionable DMPs (maDMPs) proposed by the Research Data Alliance DMP Common Standards Working Group. A maDMP corresponds to a structured representation of the most common elements present in a DMP (Miksa et al. 2020b), overcoming some obstacles linked to text-based representation. Such a structured representation makes it easier for DMPs to become readable and reusable for both humans and machines alike. The DMP Common Standard ontology (DCSO) (Cardoso et al. 2022) further supports maDMPs as it makes it easier to extend the original maDMP application profile to cover additional elements related to, for instance, SMPs or specific requirements from funders. maDMPs can be combined with the notion of a Research Object Crates (RO-Crate) to automate and ease management of research data (Miksa et al. 2020a). An RO-Crate (Soiland-Reyes et al. 2022) is an open, communitydriven, and lightweight approach based on schema.org (Guha et al. 2016) annotations in JSON-LD to package research data (or any other research digital object) together with its metadata in a machine-readable manner. The ELIXIR SMP has been developed by the ELIXIR Software Development Best Practices Group in the ELIXIR Tools Platform to support researchers in life sciences (Alves et al. 2021). The ELIXIR SMP aims at making it easier to follow research software good practices aligned to the findable, accessible, interoperable and reusable principles for research software (FAIR4RS) (Chue Hong et al. 2022) while dealing with the lifecycle of research software. Its primary goal is encouraging a wider adoption by life science researchers, and being as inclusive as possible to the various levels of technical expertise. Here we present a roadmap for ELIXIR SMPs to become a FAIR digital object (FDO) (Schultes and Wittenburg 2019) based on the extension of maDMPs and DCSO and the use of RO-Crates. FDOs have been proposed as a way to package digital objects together with their metadata, types, identifiers and operations, so they become more machineactionable and auto-contained. The current version of the ELIXIR SMP includes seven sections: accessibility and licensing, documentation, testing, interoperability, versioning, reproducibility, and recognition. Each section includes questions guiding and supporting researchers so they cover key aspects of the software lifecycle relevant to their own case. To lower the barrier and make it easier for researchers, most questions are Yes/No with some few offering a set of options. In some cases, a URL is also requested, for instance regarding the location of the documentation for end-users. Our roadmap for ELIXIR SMPs to move from a textbased questionnaire to an FDO comprises four main steps: 1. creating maSMP application profile, 2. extending DCSO, 3. mapping to schema.org, and 4. using RO-Crates. Our maSMP application profile will include the semantic representation of the structured metadata that comes from the ELIXIR SMP. We will add granularity to the current root of the DCSO (dcso:DMP), by proposing the term SMP. In addition, we will propose the term ResearchSoftware as a dcso:Dataset. Terminology related to documentation, such as "Objective" will also be considered. The objective is the Why the research software, which is crucial for their comprehensibility. We will propose the term DatasetObjective as the reason for the creation of a dataset. Source-codeRepository and Source-codeTesting are also good candidates to be part of the DCSO extension. We will extend DCSO with new classes and properties as necessary to include the software related elements mentioned in the maSMP application profile. As the ELIXIR SMP targets the life science community, we will analyze the need to add links from DCSO to ontologies describing common operations, activities, and types in this domain. One important aspect is the creation of a mapping from DCSO to schema.org. Schema.org has become a popular choice to add lightway semantics to web pages but can also be used on its own to provide metadata describing all sorts of objects. In life sciences, Bioschemas (Gray et al. 2017) offers guidelines on how to use some of the schema.org types aligned to this domain. Bioschemas includes a set of profiles, including minimum, recommended and optional properties, that have been agreed to and adopted by the community, for instance the ComputationalTool profile provides a way to describe software tools and applications. Bioschemas promotes its adoption by key resources in Life Sciences and development of tools such as the Bioschemas Markup Scraper and Extractor (BMUSE) used for the harvesting of the data (Gray et al. 2022). Our final step for ELIXIR SMPs to become an FDO is using RO-Crates to package research software together with its metadata and link it to/from its corresponding SMP. To do so, we will create an RO-Crate profile capturing the metadata needed to describe software tools including elements from the SMP. It will become a versioned living crate as research software evolves with time, particularly when new releases are published. Thanks to the RO-Crate bundling nature, where digital objects are packed together with its metadata, a software crate enriched with the elements from the SMP are a good example of an FDO as all the critical information about a software tool is bound together in a unit that can be shared with peers via FAIR registries and repositories. [ABSTRACT FROM AUTHOR]

Copyright of Medien & Kommunikationswissenschaft is the property of Nomos Verlagsgesellschaft mbH & Co. KG and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Background: Research Software is a concept that has been only recently clarified. In this paper we address the need for a similar enlightenment concerning the Research Data concept. Methods: Our contribution begins by reviewing the Research Software definition, which includes the analysis of software as a legal concept, followed by the study of its production in the research environment and within the Open Science framework. Then we explore the challenges of a data definition and some of the Research Data definitions proposed in the literature. Results: We propose a Research Data concept featuring three characteristics: the data should be produced (collected, processed, analyzed, shared & disseminated) to answer a scientific question, by a scientific team, and has yield a result published or disseminated in some article or scientific contribution of any kind. Conclusions: The analysis of this definition and the context in which it is proposed provides some answers to the Borgman’s conundrum challenges, that is, which Research Data might be shared, by whom, with whom, under what conditions, why, and to what effects. They are completed with answers to the questions: how? and where?

Background: Open Science seeks to render research outputs visible, accessible and reusable. In this context, Research Data and Research Software sharing and dissemination issues provide real challenges to the scientific community, as consequence of recent progress in political, legal and funding requirements. Methods: We take advantage from the approach we have developed in a precedent publication, in which we have highlighted the similarities between the Research Data and Research Software definitions. Results: The similarities between Research Data and Research Software definitions can be extended to propose protocols for Research Data dissemination and evaluation derived from those already proposed for Research Software dissemination and evaluation. We also analyze FAIR principles for these outputs. Conclusions: Our proposals here provide concrete instructions for Research Data and Research Software producers to make them more findable and accessible, as well as arguments to choose suitable dissemination platforms to complete the FAIR framework. Future work could analyze the potential extension of this parallelism to other kinds of research outputs that are disseminated under similar conditions to those of Research Data and Research Software, that is, without widely accepted publication procedures involving editors or other external actors and where the dissemination is usually restricted through the hands of the production team.

Critical data and metadata must be captured or created in the field, or shortly thereafter, to avoid loss. For the past 10 years, the Field Acquired Information Management Systems (FAIMS) project has developed and operated a customisable field data capture platform. Over time, we built features and approaches that incorporated the Findable, Accessible, Interoperable, and Reusable (FAIR) principles into born-digital datasets created during fieldwork. This paper synthesises our experience helping more than 40 projects adapt the FAIMS platform to nearly 70 research workflows in archaeology and other fieldwork domains. We review what elements of the FAIR Data Principles FAIMS was able to build into our software, how users received these capabilities, and what sociotechnical challenges impeded creation of FAIRer field data. Based on our experience, we argue that field data capture software can facilitate the production of FAIRer data, making those data much more Findable and Reusable, and somewhat more Accessible and Interoperable. Any such improvements, however, depend upon (1) making FAIR-data features an integral part of field data collection systems, minimising the burden imposed on researchers, and (2) researchers’ willingness to spend time and resources implementing FAIR Data Principles that do not provide immediate benefits to their research.

Background: Research Software is a concept that has been only recently clarified. In this paper we address the need for a similar enlightenment concerning the Research Data concept. Methods: Our contribution begins by reviewing the Research Software definition, which includes the analysis of software as a legal concept, followed by the study of its production in the research environment and within the Open Science framework. Then we explore the challenges of a data definition and some of the Research Data definitions proposed in the literature. Results: We propose a Research Data concept featuring three characteristics: the data should be produced (collected, processed, analyzed, shared & disseminated) to answer a scientific question, by a scientific team, and has yield a result published or disseminated in some article or scientific contribution of any kind. Conclusions: The analysis of this definition and the context in which it is proposed provides some answers to the Borgman’s conundrum challenges, that is, which Research Data might be shared, by whom, with whom, under what conditions, why, and to what effects. They are completed with answers to the questions: how? and where?

This paper aims to offer a free software program, LINCE PLUS, suitable for systematic observational studies in sports and health, conducted in natural contexts such as training, education or psychology. Using one or several videos simultaneously, different parameters such as behaviors, decision-making or strategies can be analyzed. The software includes several functionalities for studies that researchers need to utilize throughout the observational study process. Collaborative work can be accomplished by using simultaneous videos and multiple observers. The results of all research conducted by LINCE PLUS are offered inside the application in real time, enabling common calculations or including specific analysis with R language without the need for any other external tool. Moreover, LINCE PLUS shows the results of each study with interactive charts or, if needed, it exports the data to specific data analysis software programs (e.g., SAS, Excel, Theme, GSEQ 5, Hoisan). We include examples of sports and health studies that have been conducted with LINCE PLUS to show the suitability of this software program. [ABSTRACT FROM AUTHOR]

Abstract Background Computational mathematical models of biological and biomedical systems have been successfully applied to advance our understanding of various regulatory processes, metabolic fluxes, effects of drug therapies, and disease evolution and transmission. Unfortunately, despite community efforts leading to the development of SBML and the BioModels database, many published models have not been fully exploited, largely due to a lack of proper documentation or the dependence on proprietary software. To facilitate the reuse and further development of systems biology and systems medicine models, an open-source toolbox that makes the overall process of model construction more consistent, understandable, transparent, and reproducible is desired. Results and discussion We provide an update on the development of modelbase, a free, expandable Python package for constructing and analysing ordinary differential equation-based mathematical models of dynamic systems. It provides intuitive and unified methods to construct and solve these systems. Significantly expanded visualisation methods allow for convenient analysis of the structural and dynamic properties of models. After specifying reaction stoichiometries and rate equations modelbase can automatically assemble the associated system of differential equations. A newly provided library of common kinetic rate laws reduces the repetitiveness of the computer programming code. modelbase is also fully compatible with SBML. Previous versions provided functions for the automatic construction of networks for isotope labelling studies. Now, using user-provided label maps, modelbase v1.2.3 streamlines the expansion of classic models to their isotope-specific versions. Finally, the library of previously published models implemented in modelbase is growing continuously. Ranging from photosynthesis to tumour cell growth to viral infection evolution, all these models are now available in a transparent, reusable and unified format through modelbase. Conclusion With this new Python software package, which is written in currently one of the most popular programming languages, the user can develop new models and actively profit from the work of others. modelbase enables reproducing and replicating models in a consistent, tractable and expandable manner. Moreover, the expansion of models to their isotopic label-specific versions enables simulating label propagation, thus providing quantitative information regarding network topology and metabolic fluxes.

The goal of this document is to openly contribute with our comments to the EOSCArchitecture report: Scholarly Infrastructures for Research Software (SIRS), and thus, to participate in the European Open Science Cloud (EOSC) architecture design.

Background: Open Science seeks to render research outputs visible, accessible and reusable. In this context, Research Data and Research Software sharing and dissemination issues provide real challenges to the scientific community, as consequence of recent progress in political, legal and funding requirements. Methods: We take advantage from the approach we have developed in a precedent publication, in which we have highlighted the similarities between the Research Data and Research Software definitions. Results: The similarities between Research Data and Research Software definitions can be extended to propose protocols for Research Data dissemination and evaluation derived from those already proposed for Research Software dissemination and evaluation. We also analyze FAIR principles for these outputs. Conclusions: Our proposals here provide concrete instructions for Research Data and Research Software producers to make them more findable and accessible, as well as arguments to choose suitable dissemination platforms to complete the FAIR framework. Future work could analyze the potential extension of this parallelism to other kinds of research outputs that are disseminated under similar conditions to those of Research Data and Research Software, that is, without widely accepted publication procedures involving editors or other external actors and where the dissemination is usually restricted through the hands of the production team.

Background: Research Software is a concept that has been only recently clarified. In this paper we address the need for a similar enlightenment concerning the Research Data concept. Methods: Our contribution begins by reviewing the Research Software definition, which includes the analysis of software as a legal concept, followed by the study of its production in the research environment and within the Open Science framework. Then we explore the challenges of a data definition and some of the Research Data definitions proposed in the literature. Results: We propose a Research Data concept featuring three characteristics: the data should be produced (collected, processed, analyzed, shared & disseminated) to answer a scientific question, by a scientific team, and has yield a result published or disseminated in some article or scientific contribution of any kind. Conclusions: The analysis of this definition and the context in which it is proposed provides some answers to the Borgman’s conundrum challenges, that is, which Research Data might be shared, by whom, with whom, under what conditions, why, and to what effects. They are completed with answers to the questions: how? and where?

Research software is a critical component of contemporary scholarship. Yet, most research software is developed and managed in ways that are at odds with its long-term sustainability. This paper presents findings from a survey of 1,149 researchers, primarily from the United States, about sustainability challenges they face in developing and using research software. Some of our key findings include a repeated need for more opportunities and time for developers of research software to receive training. These training needs cross the software lifecycle and various types of tools. We also identified the recurring need for better models of funding research software and for providing credit to those who develop the software so they can advance in their careers. The results of this survey will help inform future infrastructure and service support for software developers and users, as well as national research policy aimed at increasing the sustainability of research software.

Small to medium-scale data science experiments often rely on research software developed ad-hoc by individual scientists or small teams. Often there is no time to make the research software fast, reusable, and open access. The consequence is twofold. First, subsequent researchers must spend significant work hours building upon the proposed hypotheses or experimental framework. In the worst case, others cannot reproduce the experiment and reuse the findings for subsequent research. Second, suppose the ad-hoc research software fails during often long-running computational expensive experiments. In that case, the overall effort to iteratively improve the software and rerun the experiments creates significant time pressure on the researchers. We suggest making caching an integral part of the research software development process, even before the first line of code is written. This article outlines caching recommendations for developing research software in data science projects. Our recommendations provide a perspective to circumvent common problems such as propriety dependence, speed, etc. At the same time, caching contributes to the reproducibility of experiments in the open science workflow. Concerning the four guiding principles, i.e., Findability, Accessibility, Interoperability, and Reusability (FAIR), we foresee that including the proposed recommendation in a research software development will make the data related to that software FAIRer for both machines and humans. We exhibit the usefulness of some of the proposed recommendations on our recently completed research software project in mathematical information retrieval.

Software development for data reduction and analysis at large research facilities is increasingly professionalized, and internationally coordinated. To foster software quality and sustainability, and to facilitate collaboration, representatives from software groups of European neutron and muon facilities have agreed on a set of guidelines for development practices, infrastructure, and functional and non-functional product properties. These guidelines have been derived from actual practices in software projects from the EU funded consortium 'Science and Innovation with Neutrons in Europe in 2020' (SINE2020), and have been enriched through extensive literature review. Besides guiding the work of the professional software engineers in our computing groups, we hope to influence scientists who are willing to contribute their own data treatment software to our community. Moreover, this work may also provide inspiration to scientific software development beyond the neutron and muon field. [ABSTRACT FROM AUTHOR]

Context: Research software is software developed by and/or used by researchers, across a wide variety of domains, to perform their research. Because of the complexity of research software, developers cannot conduct exhaustive testing. As a result, researchers have lower confidence in the correctness of the output of the software. Peer code review, a standard software engineering practice, has helped address this problem in other types of software. Objective: Peer code review is less prevalent in research software than it is in other types of software. In addition, the literature does not contain any studies about the use of peer code review in research software. Therefore, through analyzing developers perceptions, the goal of this work is to understand the current practice of peer code review in the development of research software, identify challenges and barriers associated with peer code review in research software, and present approaches to improve the peer code review in research software. Method: We conducted interviews and a community survey of research software developers to collect information about their current peer code review practices, difficulties they face, and how they address those difficulties. Results: We received 84 unique responses from the interviews and surveys. The results show that while research software teams review a large amount of their code, they lack formal process, proper organization, and adequate people to perform the reviews. Conclusions: Use of peer code review is promising for improving the quality of research software and thereby improving the trustworthiness of the underlying research results. In addition, by using peer code review, research software developers produce more readable and understandable code, which will be easier to maintain. [ABSTRACT FROM AUTHOR]