This article details a correction to: Gandhi, S., Diggs, S., Córdoba, M.A., Bezuidenhout, L., Cobe, R., El Jadid, S., Peterson, B., Quick, R., Shanahan, H., Venkataraman, S., Okorafor, E. and Van den Eynden, V. (2026) ‘Building Responsible and Sustainable Open Data Literacy Skills for Early Career Researchers: A Decade of the SoRDS Programme’, Data Science Journal, 25(1), p. 12. https://doi.org/10.5334/dsj-2026-012.

Given the wide variability in the quality of next-generation sequencing data submitted to public repositories, it is essential to identify methods that can perform quality control on these data sets when additional quality control data, such as mean tile data, are missing from public repositories. In this study, we present evidence that correlating counts of reads corresponding to pairs of motifs separated over specific distances on individual exons can be used as a proxy mean tile data in the data sets we analyzed and hence could be used when mean tile data are not available. As test data sets we use the Homo sapiens in vitro transcribed (IVT) data set, and a Drosophila melanogaster data set comprising wild and mutant types. We find that a FastQC analysis of the available parts of these data sets demonstrates that the per-tile sequencing quality is good for all the data sets apart from the mutant-type data where the mutant-r3 data are worse than the mutant-r2 data. Correspondingly, intra-exon motif correlations are reasonably large for all data sets except this latter case where the mutant-r2 correlations are low and the mutant-r3 correlations close to zero. We propose that these extremely low correlations are indicative of bias of technical origin, such as flowcell errors. In addition to this, the intra-exon motif correlations as a function of both guanosine-cytosine (GC) content parameters are somewhat higher and less dependent on the GC content parameters in the IVT-Plasmids messenger RNA (mRNA) selection free RNA-Seq sample (control) than in the other RNA-Seq samples that did undergo mRNA selection: both ribosomal depletion (IVT-Only) and PolyA selection (IVT-PolyA, wild type, and mutant).

We investigate the chiral properties of quenched domain-wall QCD (DWQCD) at the lattice spacings a ≃ 1 and 2 GeV for both plaquette and renormalization-group (RG) improved gauge actions. In the case of the plaquette action we find that the quark mass defined through the axial WardTakahashi identity remains non-vanishing in the DWQCD chiral limit that the bare quark mass mf → 0 and the length of the fifth dimension Ns → ∞, indicating that chiral symmetry is not realized with quenched DWQCD up to a ≃ 2 GeV. The behavior is much improved for the RG-improved gauge action: while a non-vanishing quark mass remains in the chiral limit at a ≃ 1 GeV, the result at a ≃ 2 GeV is consistent with an exponentially vanishing quark mass in the DWQCD chiral limit, indicating the realization of exact chiral symmetry. An interpretation and implications are briefly discussed. Typeset using REVTEX

The FAIR data principles are rapidly becoming a standard through which to assess responsible and reproducible research. In contrast to the requirements associated with the Interoperability principle, the requirements associated with the Accessibility principle are often assumed to be relatively straightforward to implement. Indeed, a variety of different tools assessing FAIR rely on the data being deposited in a trustworthy digital repository. In this paper we note that there is an implicit assumption that access to a repository is independent of where the user is geographically located. Using a virtual personal network (VPN) service we find that access to a set of web sites that underpin Open Science is variable from a set of 14 countries; either through connectivity issues (i.e., connections to download HTML being dropped) or through direct blocking (i.e., web servers sending 403 error codes). Many of the countries included in this study are already marginalized from Open Science discussions due to political issues or infrastructural challenges. This study clearly indicates that access to FAIR data resources is influenced by a range of geo-political factors. Given the volatile nature of politics and the slow pace of infrastructural investment, this is likely to continue to be an issue and indeed may grow. We propose that it is essential for discussions and implementations of FAIR to include awareness of these issues of accessibility. Without this awareness, the expansion of FAIR data may unintentionally reinforce current access inequities and research inequalities around the globe.

The CODATA-RDA Schools for Research Data Science (SRDS) is a network of schools originating in the RDA in 2016. In 2019 it was recognized as an RDA output. To date, over 400 students from 40 countries have been trained in 10 schools. The majority of these students were postgraduates from low/middle-income countries (LMICs). In contrast to many other data science training approaches, the SRDS schools are designed to be 2-week, disciplinarily-agnostic, residential events where students are introduced to a broad range of tools requisite for efficient and responsible data-centric research. This paper presents the results of a survey carried out on alumni from schools held between 2016 and 2019 (45% response). The results of the survey strongly support the SRDS’s long-term goals of facilitating data science training/capacity building within LMICs, and to foster communities of early career researchers (ECRs) conducting responsible and open data science research. The survey results demonstrated that 90% of respondent alumni continued to conduct research and make use of the skills acquired at the SRDS. Modules on open and responsible research and research data management were rated as important for future research. 79% of respondents confirmed that they maintained contact with peers, and 31% had set up academic collaborations with peers and/or instructors. Many had gone on to present content from the schools in their home institutions. The survey results clearly demonstrate the impact of the SRDS, and the value of an expanding network of schools supported by the RDA and CODATA.

We evaluate recent efforts to further the effective teaching of FAIR data principles by examining existing and developing educational frameworks focused upon FAIR, training initiatives that have informed teaching on FAIR skills' topics, and a number of key sources for discovering FAIR training materials and how much those sources provide descriptive information about the materials. FAIR4S, providing a coherent description of skills and competencies, is analyzed by target audience using the description of actors found in a European Open Science Cloud ecosystem report and by comparison of the coverage and extent of description of educational and training materials available from the list of sources for finding such materials. Our analysis elucidates the importance of linking resources to FAIR-related educational frameworks, providing consistent descriptions of them using a community-based metadata scheme, and developing an instructor community of practice where ideas and methods can be shared on how to teach FAIR data skills.

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for clinicians, researchers, policy- and decision-makers, funders, publishers, public health experts, disaster preparedness and response experts, infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations), and other potential users. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.

The systemic challenges of the COVID-19 pandemic require cross-disciplinary collaboration in a global and timely fashion. Such collaboration needs open research practices and the sharing of research outputs, such as data and code, thereby facilitating research and research reproducibility and timely collaboration beyond borders. The Research Data Alliance COVID-19 Working Group recently published a set of recommendations and guidelines on data sharing and related best practices for COVID-19 research. These guidelines include recommendations for researchers, policymakers, funders, publishers and infrastructure providers from the perspective of different domains (Clinical Medicine, Omics, Epidemiology, Social Sciences, Community Participation, Indigenous Peoples, Research Software, Legal and Ethical Considerations). Several overarching themes have emerged from this document such as the need to balance the creation of data adherent to FAIR principles (findable, accessible, interoperable and reusable), with the need for quick data release; the use of trustworthy research data repositories; the use of well-annotated data with meaningful metadata; and practices of documenting methods and software. The resulting document marks an unprecedented cross-disciplinary, cross-sectoral, and cross-jurisdictional effort authored by over 160 experts from around the globe. This letter summarises key points of the Recommendations and Guidelines, highlights the relevant findings, shines a spotlight on the process, and suggests how these developments can be leveraged by the wider scientific community.

Thousands of community-developed (meta)data guidelines, models, ontologies, schemas and formats have been created and implemented by several thousand data repositories and knowledge-bases, across all disciplines. These resources are necessary to meet government, funder and publisher expectations of greater transparency and access to and preservation of data related to research publications. This obligates researchers to ensure their data is FAIR, share their data using the appropriate standards, store their data in sustainable and community-adopted repositories, and to conform to funder and publisher data policies. FAIR data sharing also plays a key role in enabling researchers to evaluate, re-analyse and reproduce each other’s work. We can map the landscape of relationships between community-adopted standards and repositories, and the journal publisher and funder data policies that recommend their use. In this paper, we show how the work of the GO-FAIR FAIR Standards, Repositories and Policies (StRePo) Implementation Network serves as a central integration and cross-fertilisation point for the reuse of FAIR standards, repositories and data policies in general. Pivotal to this effort, the FAIRsharing, an endorsed flagship resource of the Research Data Alliance that maps the landscape of relationships between community-adopted standards and repositories, and the journal publisher and funder data policies that recommend their use. Lastly, we highlight a number of activities around FAIR tools, services and educational efforts to raise awareness and encourage participation.

Data science skills are rapidly becoming a necessity in modern science. In response to this need, institutions and organizations around the world are developing research data science curricula to teach the programming and computational skills that are needed to build and maintain data infrastructures and maximize the use of available data. To date, however, few of these courses have included an explicit ethics component, and developing such components can be challenging. This paper describes a novel approach to teaching data ethics on short courses developed for the CODATA-RDA Schools for Research Data Science. The ethics content of these schools is centred on the concept of open and responsible (data) science citizenship that draws on virtue ethics to promote ethics of practice. Despite having little formal teaching time, this concept of citizenship is made central to the course by distributing ethics content across technical modules. Ethics instruction consists of a wide range of techniques, including stand-alone lectures, group discussions and mini-exercises linked to technical modules. This multi-level approach enables students to develop an understanding both of “responsible and open (data) science citizenship”, and of how such responsibilities are implemented in daily research practices within their home environment. This approach successfully locates ethics within daily data science practice, and allows students to see how small actions build into larger ethical concerns. This emphasises that ethics are not something “removed from daily research” or the remit of data generators/end users, but rather are a vital concern for all data scientists.

Given the expected increase in demand for Data Stewards and Data Stewardship skills it is clear that there is a need to develop training, education and CPD (continuous professional development) in this area.In this paper a brief introduction is provided to the origin of definitions of Data Stewardship. Also it notes the present tendency towards equivalence between Data Stewardship skills and FAIR principles. It then focuses on one specific training event – the pilot Data Stewardship strand of the CODATA-RDA Research Data Science schools that by the time of the IDCC meeting will have been held in Trieste in August 2019. The paper will discuss the overall curriculum for the pilot school, how it matches with the FAIR4S framework, and plans for getting feedback from the students.Finally, the paper discuss future plans for the school, in particular how to deepen the integration between the Data Stewardship strand with the Early Career Researcher strand.

This document TrainingInTheEOSC_WorkshopReport_01_00.pdf is the output of a three-day workshop on Training in the EOSC, 26-28 February 2020, The Hague, The Netherlands. The workshop was organised by DANS in collaboration with EUDAT, EGI and OpenAIRE. It provides recommendations regarding Rules of Participation for training as well as recommendations regarding practical guidance for training service providers.

There is a growing acknowledgement in the scientific community of the importance of making experimental data machine findable, accessible, interoperable, and reusable (FAIR). Recognizing that high quality metadata are essential to make datasets FAIR, members of the GO FAIR Initiative and the Research Data Alliance (RDA) have initiated a series of workshops to encourage the creation of Metadata for Machines (M4M), enabling any self-identified stakeholder to define and promote the reuse of standardized, comprehensive machine-actionable metadata. The funders of scientific research recognize that they have an important role to play in ensuring that experimental results are FAIR, and that high quality metadata and careful planning for FAIR data stewardship are central to these goals. We describe the outcome of a recent M4M workshop that has led to a pilot programme involving two national science funders, the Health Research Board of Ireland (HRB) and the Netherlands Organisation for Health Research and Development (ZonMW). These funding organizations will explore new technologies to define at the time that a request for proposals is issued the minimal set of machine-actionable metadata that they would like investigators to use to annotate their datasets, to enable investigators to create such metadata to help make their data FAIR, and to develop data-stewardship plans that ensure that experimental data will be managed appropriately abiding by the FAIR principles. The FAIR Funders design envisions a data-management workflow having seven essential stages, where solution providers are openly invited to participate. The initial pilot programme will launch using existing computer-based tools of those who attended the M4M Workshop.

RNA-seq is a high-throughput Next-sequencing technique for estimating the concentration of all transcripts in a transcriptome. The method involves complex preparatory and post-processing steps which can introduce bias, and the technique produces a large amount of data [7, 19]. Two important challenges in processing RNA-seq data are therefore the ability to process a vast amount of data, and methods to quantify the bias in public RNA-seq datasets. We describe a novel analysis method, based on analysing sequence motif correlations, that employs MapReduce on Apache Spark to quantify bias in Next-generation sequencing (NGS) data at the deep exon level. Our implementation is designed specifically for processing large datasets and allows for scalability and deployment on cloud service providers offering MapReduce. In investigating the wild and mutant organism types in the species D. melanogaster we have found that motifs with runs of Gs (or their complement) exhibit low motif-pair correlations in comparison with other motif-pairs. This is independent of the mean exon GC content in the wild type data, but there is a mild dependence in the mutant data. Hence, whilst both datasets show the same trends, there is however significant variation between the two samples.

Metagenomics is the science of analysing the structure and function of DNA samples taken from the environment (e.g. soil or human gut) as opposed to a single organism. So far, researchers have used traditional genomics tools and pipelines applied to metagenomics analysis such as species identification, sequence alignment and assembly. In addition to being computationally expensive, these approaches lack an emphasis on the functional profile of the sample regardless of species diversity, and how it changes under different conditions. It also ignores unculturable species and genes undergoing horizontal transfer. We propose a new pipeline based on taking a “systems” approach to metagenomics analysis, in this case to analyse human gut microbiome data. Instead of identifying existing species, we examine a sample as a self-contained, open system with a distinct functional profile. The pipeline was used to analyse data from an experiment performed on the gut microbiomes of lean, obese and overweight twins. Previous analysis of this data only focused on taxonomic binning. Using our systems metagenomics approach, our analysis found two very different functional profiles for lean and obese twins, with obese ones being distinctly more diverse. There are also interesting differences in metabolic pathways which could indicate specific driving forces for obesity.

The paper reviews the use of the Hadoop platform in structural bioinformatics applications. For structural bioinformatics, Hadoop provides a new framework to analyse large fractions of the Protein Data Bank that is key for high-throughput studies of, for example, protein-ligand docking, clustering of protein-ligand complexes and structural alignment. Specifically we review in the literature a number of implementations using Hadoop of high-throughput analyses and their scalability. We find that these deployments for the most part use known executables called from MapReduce rather than rewriting the algorithms. The scalability exhibits a variable behaviour in comparison with other batch schedulers, particularly as direct comparisons on the same platform are generally not available. Direct comparisons of Hadoop with batch schedulers are absent in the literature but we note there is some evidence that Message Passing Interface implementations scale better than Hadoop. A significant barrier to the use of the Hadoop ecosystem is the difficulty of the interface and configuration of a resource to use Hadoop. This will improve over time as interfaces to Hadoop, e.g. Spark improve, usage of cloud platforms (e.g. Azure and Amazon Web Services (AWS)) increases and standardised approaches such as Workflow Languages (i.e. Workflow Definition Language, Common Workflow Language and Nextflow) are taken up.

Detecting sources of bias in transcriptomic data is essential to determine signals of Biological significance. We outline a novel method to detect sequence specific bias in short read Next Generation Sequencing data. This is based on determining intra-exon correlations between specific motifs. This requires a mild assumption that short reads sampled from specific regions from the same exon will be correlated with each other. This has been implemented on Apache Spark and used to analyse two D. melanogaster eye-antennal disc data sets generated at the same laboratory. The wild type data set in drosophila indicates a variation due to motif GC content that is more significant than that found due to exon GC content. The software is available online and could be applied for cross-experiment transcriptome data analysis in eukaryotes.