The dataset contains the text of the documents that are sources of evidence used in [1] and [2] to distill our reference scenarios according to the methodology suggested by Yin in [3].

The dataset is composed of 95 unique document texts spanning the period 2005-2022. This dataset makes available a corpus of documentary sources useful for outlining case studies related to scenarios in which the DPO finds himself operating in the performance of his daily activities.

The language used in the corpus is mainly Italian, but some documents are in English and French. For the reader's benefit, we provide an English translation of the title of each document.

The documentary sources are of many types (for example, court decisions, supervisory authorities' decisions, job advertisements, and newspaper articles), provided by different bodies (such as supervisor authorities, data controllers, European Union institutions, private companies, courts, public authorities, research organizations, newspapers, and public administrations), and redacted from distinct professional roles (for example, data protection officers, general managers, university rectors, collegiate bodies, judges, and journalists).

The documentary sources were collected from 31 different bodies. Most of the documents in the corpus (a total of 83 documents) have been transformed into Rich Text Format (RTF), while the other documents (a total of 12) are in PDF format. All the documents have been manually read and verified. The dataset is helpful as a starting point for a case studies analysis on the daily issues a data protection officer face. Details on the methodology can be found in the accompanying papers.

The available files are as follows:

documents-texts.zip --> contain a directory of .rtf files (in some cases .pdf files) with the text of documents used as sources for the case studies. Each file has been renamed with its SHA1 hash so that it can be easily recognized.

documents-metadata.csv --> Contains a CSV file with the metadata for each document used as a source for the case studies.

This dataset is the original one used in the publication [1] and the preprint containing the additional material [2].

[1] F. Ciclosi and F. Massacci, "The Data Protection Officer: A Ubiquitous Role That No One Really Knows" in IEEE Security & Privacy, vol. 21, no. 01, pp. 66-77, 2023, doi: 10.1109/MSEC.2022.3222115, url: https://doi.ieeecomputersociety.org/10.1109/MSEC.2022.3222115.

[2] F. Ciclosi and F. Massacci, "The Data Protection Officer, an ubiquitous role nobody really knows." arXiv preprint arXiv:2212.07712, 2022.

[3] R. K. Yin, Case study research and applications. Sage, 2018.

IntroductionHalfway to the deadline of the 2030 agenda, humankind continues to face long-standing yet urgent policy and management challenges to address resource shortages and deliver on Sustainable Development Goal 3; health and well-being for all at all ages. More than half of the global population lacks access to essential health services. Additional resources are required and need to be allocated effectively and equitably. Resource allocation models, however, have struggled to accurately predict effects and to present optimal allocations, thus hampering effectiveness and equity improvement. The current advances in machine learning present opportunities to better predict allocation effects and to prescribe solutions that better balance effectiveness and equity. The most advanced of these models tend to be “black box” models that lack explainability. This lack of explainability is problematic as it can clash with professional values and hide biases that negatively impact effectiveness and equity.MethodsThrough a novel theoretical framework and two diverse case studies, this manuscript explores the trade-offs between effectiveness, equity, and explainability. The case studies consider family planning in a low income country and kidney allocation in a high income country.ResultsBoth case studies find that the least explainable models hardly offer improvements in effectiveness and equity over explainable alternatives.DiscussionAs this may more widely apply to health resource allocation decisions, explainable analytics, which are more likely to be trusted and used, might better enable progress towards SDG3 for now. Future research on explainability, also in relation to equity and fairness of allocation policies, can help deliver on the promise of advanced predictive and prescriptive analytics.

This paper presents the methodology of a PhD project on identity development in German dual track VET. The mixed methods approach comprises theory-driven longitudinal case studies using qualitative and quantitative data and a document analysis of curricula. The study was part of a larger design-based research project and aimed to evaluate a newly developed and implemented VET curriculum for the retail sector from the learners’ point of view. The new curriculum contained a special competence dimension to foster vocational identity development and a major interest was to investigate the extent to which the curriculum succeeded to do so. The paper will start with a summary and assessment of limitations and advantages of research designs applied in different existing studies examining identity development. The author will then outline identity-relevant theories and approaches that proved to be useful for her research interest. Subsequently, she will describe the resulting design for collecting and analyzing her data. The core result consists of a theme-centered process analysis visualizing the individual state of developmental tasks. With the presentation of three exemplary cases she will illustrate how her approach allowed her to deeply understand the identity of the respective person and at the same time generate general insights about identity development of learners in VET. Finally, strengths and limitations of her approach will be discussed. This research approach is also suited to supporting pedagogical design, representing major added value.

This dataset contains supporting information for the survey conducted by the Physical Science Data Infrastructure (PSDI) Initiative www.psdi.ac.uk. There were 8 case studies conducted as part of this project, this survey was designed predominantly by the researchers in case study 4, in conjunction with collaborators from case study 8. This survey was designed to collect data about current working practices in the physical sciences domain, looking at how scientists are recording and sharing their data, what software they use, what structures they work with, what features they require and how things have changed since the COVID-19 pandemic. Ethics approval was obtained from the University of Southampton Ethics and Research Governance Team through application ERGO/FEPS/70431 and our participants were provided with our participant information sheet and data protection plan prior to their participation in the survey. As part of this work an ELN Landscape Assessment was also conducted to extend the work done as part of this paper: https://jcheminf.biomedcentral.com/articles/10.1186/s13321-017-0221-3 This dataset includes the following data - PSDI_FullListofFeatures.pdf: Full list of features taken extrapolated from the survey and previous research - PSDI_CategorisedSoftware.pdf: Categorised List of software taken from the survey - PSDI_CategorisedFeatures.pdf: Categorised List of features extrapolated from the survey and previous research with detailed explanations - ELNLandscapeAssessment.xlsx: ELN Data collected as part of this project This version of the dataset, 10.5258/SOTON/D2438v1, was updated on 2024/02/15. The previous version is available at 10.5258/SOTON/D2438

The data provided was collected in the context of a meta analytical review entitled: “Effects of Shared Text Reading for Students With Intellectual Disability: A Meta-analytical Review of Instructional Strategies”conducted by R. Sermier Dessemontet, M. Geyer, A-L. Linder, M. Atzemian, C. Martinet, N. Meuli, C. Audrin, and A-F. de Chambrier, and published in the journal “Educational Research Review”.. The objective of the meta-analytical review was to measure the effect of shared text reading on the listening comprehension skills of students with ID and to identify efficient instructional strategies to teach these students listening comprehension. 22 single-case experimental studies were included in this meta-analysis. Data from each participant in each study was extracted and transformed into percentages of independent correct responses. Effect size estimates were performed based on these percentages.

The shared data contains the following: raw data from each participant used for effect size estimate calculations, a table presenting effect size estimates and moderator-coding for each participant in each study, and the statistical analysis script. A pdf describing meta-data (meta-data_meta-analysis.pdf) is also provided for more information on each available document and describes data.

IntroductionHigh-throughput sequencing (HTS) provides an efficient and cost-effective way to generate large amounts of sequence data, providing a very powerful tool to analyze biodiversity of soil organisms. However, marker-based methods and the resulting datasets come with a range of challenges and disputes, including incomplete reference databases, controversial sequence similarity thresholds for delimitating taxa, and downstream compositional data analysis. MethodsHere, we use HTS data from a soil nematode biodiversity experiment to explore standardized HTS data processing procedures. We compared the taxonomic assignment performance of two main rDNA reference databases (SILVA and PR2). We tested whether the same ecological patterns are detected with Amplicon Sequence Variants (ASV; 100% similarity) versus classical Operational Taxonomic Units (OTU; 97% similarity). Further, we tested how different HTS data normalization methods affect the recovery of beta diversity patterns and the identification of differentially abundant taxa.ResultsAt this time, the SILVA 138 eukaryotic database performed better than the PR2 4.12 database, assigning more reads to family level and providing higher phylogenetic resolution. ASV- and OTU-based alpha and beta diversity of nematodes correlated closely, indicating that OTU-based studies represent useful reference points. For downstream data analyses, our results indicate that loss of data during subsampling under rarefaction-based methods might reduce the sensitivity of the method, e.g. underestimate the differences between nematode communities under different treatments, while the clr-transformation-based methods may overestimate effects. The Analysis of Compositions of Microbiome with Bias Correction approach (ANCOM-BC) retains all data and accounts for uneven sampling fractions for each sample, suggesting that this is currently the optimal method to analyze compositional data.DiscussionOverall, our study highlights the importance of comparing and selecting taxonomic reference databases before data analyses, and provides solid evidence for the similarity and comparability between OTU- and ASV-based nematode studies. Further, the results highlight the potential weakness of rarefaction-based and clr-transformation-based methods. We recommend future studies use ASV and that both the taxonomic reference databases and normalization strategies are carefully tested and selected before analyzing the data.

Meta-analysis is of increasing importance as this quantitative synthesis technique has the potential to summarize a tremendous amount of research evidence, which can help making evidence-based decisions in policy, practice, and theory. This paper examines the single-case meta-analyses within the Education and Psychology fields. The amount of methodological studies related to the meta-analysis of Single-Case Experimental Designs (SCEDs) is increasing rapidly, especially in these fields. This underscores the necessity of a succinct summary to help methodologists identify areas for further development in Education and Psychology research. It also aids applied researchers and research synthesists in discerning when to use meta-analytic techniques for SCED studies based on criteria such as bias, mean squared error, 95% confidence intervals, Type I error rates, and statistical power. Based on the summary of empirical evidence from 18 reports identified through a systematic search procedure, information related to meta-analytic techniques, data generation and analysis models, design conditions, statistical properties, conditions under which the meta-analytic technique is appropriate, and the study purpose(s) were extracted. The results indicate that three-level hierarchical linear modeling is the most empirically validated SCED meta-analytic technique, and parameter bias is the most prominent statistical property investigated. A large number of primary studies (more than 30) and at least 20 measurement occasions per participant are recommended for usage of SCED meta-analysis in Education and Psychology fields.