Institutional Conditions That Matter to Community College Students\u27 Success: A Multiple-Case Study

The purpose of this qualitative and explanatory study utilized a multiple-case study to better understand the impact of institutional conditions that contributed to continuing students’ success at Georgia Highlands College (GHC) in terms of identifying and making progress toward or achieving educational goals from the student perspective. The researcher compared two locations at GHC in northwest Georgia, a nonresidential, limited-access state college within the University System of Georgia. This study was intended to ascertain students’ perspectives regarding what they need from institutions to succeed. The researcher examined the roles of campus environment and institutional agents (faculty, staff, and administrators) to determine what matters to students’ success at GHC. A cross-case analysis revealed similar findings at each location in that students identified three themes, which were institutional characteristics, environment conducive for learning, and meaningful interactions with institutional agents, as important to their success

Overview and Analysis of Practices with Open Educational Resources in Adult Education in Europe

OER4Adults aimed to provide an overview of Open Educational Practices in adult learning in Europe, identifying enablers and barriers to successful implementation of practices with OER. The project was conducted in 2012-2013 by a team from the Caledonian Academy, Glasgow Caledonian University, funded by The Institute for Prospective Technological Studies (IPTS). The project drew on data from four main sources: • OER4Adults inventory of over 150 OER initiatives relevant to adult learning in Europe • Responses from the leaders of 36 OER initiatives to a detailed SWOT survey • Responses from 89 lifelong learners and adult educators to a short poll • The Vision Papers on Open Education 2030: Lifelong Learning published by IPTS Interpretation was informed by interviews with OER and adult education experts, discussion at the IPTS Foresight Workshop on Open Education and Lifelong Learning 2030, and evaluation of the UKOER programme. Analysis revealed 6 tensions that drive developing practices around OER in adult learning as well 6 summary recommendations for the further development of such practices

Laboratory requirements for in-situ and remote sensing of suspended material

Recommendations for laboratory and in-situ measurements required for remote sensing of suspended material are presented. This study investigates the properties of the suspended materials, factors influencing the upwelling radiance, and the various types of remote sensing techniques. Calibration and correlation procedures are given to obtain the accuracy necessary to quantify the suspended materials by remote sensing. In addition, the report presents a survey of the national need for sediment data, the agencies that deal with and require the data of suspended sediment, and a summary of some recent findings of sediment measurements

Open education research: from the practical to the theoretical

Open Educational Resources (OER) remove restrictions for learners and educators by their nature. OER are free of direct cost to the end user and, in most cases, are openly accessible online. The recent growth in interest in OER has several drivers, ideological, political and economic, none of which in itself explains how learning will be supported or help us to develop effective models and learning design. Looking at existing approaches that are taking OER from resources into practice and at some of the theories for learning that have been proposed in recent times, we can see that OER offer attractive affordances for the expansion of formal education and for the support of informal learning

Towards Reproducible and Privacy-preserving Analyses Across Federated Repositories for Omics data

Even when duly anonymized, health research data has the potential to be disclosive and there- fore requires special safeguards according to the European General Data Protection Regulation (GDPR). Furthermore, the incorporation of FAIR principles (Findable, Accessible, Interoperable, Reusable) for a more favorable reuse of existing data, calls for an approach where sensitive data is kept locally and only metadata and aggregated results are shared. Additionally, since central pool- ing is discouraged by ethical, legal, and societal issues, it is more frequent to observe maturing data management frameworks, and platforms adopting the federated approach. Current implementations of privacy-preserving analysis frameworks seem to be limited when data becomes very large (millions of rows, hundreds of variables). Biological samples data, col- lected by high-throughput technologies, such as Next Generation Sequencing (NGS), which allows to sequence entire genomes, are examples of this kind of data. The term "genomics" refers to the field of science that studies genomes. The Omics tech- nologies intend to produce a systematic identification of all mRNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics), respectively, present in a given biological sample. In the particular case of Omics data, these data are produced by computational workflows known as bioinformatics pipelines. The reproducibility of these pipelines is hard and it is often underestimated. Nevertheless, it is important to generate trust in scientific results, and therefore, is fundamental to know how these Omics data were generated or obtained. This work will leverage on the promising results of current open-source implementations for distributed privacy-preserving analyses, while aiming at generalizing the approach and addressing some of their shortcomings. To enable the privacy-preserving analysis of Omics data, we introduced the "resource" con- cept, implemented in one of the studied solutions. The results were promising, seeing that the privacy-preserving analysis was effective when us- ing the DataSHIELD framework in conjunction with the "resource R" package. We also concluded that the adoption of specialized DataSHIELD packages for Omics analyses is a viable pathway to leverage the privacy-preserving for Omics data. To address the reproducibility challenges, we defined a database model to represent the steps, commands and operations executed by the bioinformatics pipelines. The database model is promising, but to accomplish all reproducibility requirements, including container support and integration with code sharing platforms, it is necessary to use other tools, such as Nextflow or Snakemake, with dozens of other tested and mature functions.Even when duly anonymized, health research data has the potential to be disclosive and there- fore requires special safeguards according to the European General Data Protection Regulation (GDPR). Furthermore, the incorporation of FAIR principles (Findable, Accessible, Interoperable, Reusable) for a more favorable reuse of existing data, calls for an approach where sensitive data is kept locally and only metadata and aggregated results are shared. Additionally, since central pool- ing is discouraged by ethical, legal, and societal issues, it is more frequent to observe maturing data management frameworks, and platforms adopting the federated approach. Current implementations of privacy-preserving analysis frameworks seem to be limited when data becomes very large (millions of rows, hundreds of variables). Biological samples data, col- lected by high-throughput technologies, such as Next Generation Sequencing (NGS), which allows to sequence entire genomes, are examples of this kind of data. The term "genomics" refers to the field of science that studies genomes. The Omics tech- nologies intend to produce a systematic identification of all mRNA (transcriptomics), proteins (proteomics), and metabolites (metabolomics), respectively, present in a given biological sample. In the particular case of Omics data, these data are produced by computational workflows known as bioinformatics pipelines. The reproducibility of these pipelines is hard and it is often underestimated. Nevertheless, it is important to generate trust in scientific results, and therefore, is fundamental to know how these Omics data were generated or obtained. This work will leverage on the promising results of current open-source implementations for distributed privacy-preserving analyses, while aiming at generalizing the approach and addressing some of their shortcomings. To enable the privacy-preserving analysis of Omics data, we introduced the "resource" con- cept, implemented in one of the studied solutions. The results were promising, seeing that the privacy-preserving analysis was effective when us- ing the DataSHIELD framework in conjunction with the "resource R" package. We also concluded that the adoption of specialized DataSHIELD packages for Omics analyses is a viable pathway to leverage the privacy-preserving for Omics data. To address the reproducibility challenges, we defined a database model to represent the steps, commands and operations executed by the bioinformatics pipelines. The database model is promising, but to accomplish all reproducibility requirements, including container support and integration with code sharing platforms, it is necessary to use other tools, such as Nextflow or Snakemake, with dozens of other tested and mature functions

Laboratory requirements for in-situ and remote sensing of suspended material

Recommendations for laboratory and in-situ measurements required for remote sensing of suspended material are presented. This study investigates the properties of the suspended materials, factors influencing the upwelling radiance, and the various types of remote sensing techniques. Calibration and correlation procedures are given to obtain the accuracy necessary to quantify the suspended materials by remote sensing. In addition, the report presents a survey of the national need for sediment data, the agencies that deal with and require the data of suspended sediment, and a summary of some recent findings of sediment measurements

A European Platform for Distributed Real Time Modelling & Simulation of Emerging Electricity Systems

This report presents the proposal for the constitution of a European platform consisting of the federation of real-time modelling and simulation facilities applied to the analysis of emerging electricity systems. Such a platform can be understood as a pan-European distributed laboratory aiming at making use of the best available relevant resources and knowledge for the sake of supporting industry and policy makers and conducting advanced scientific research. The report describes the need for such a platform, with reference to the current status of power systems; the state of the art of the relevant technologies; and the character and format that the platform might take. This integrated distributed laboratory will facilitate the modelling, testing and assessment of power systems beyond the capacities of each single entity, enabling remote access to software and equipment anywhere in the EU, by establishing a real-time interconnection to the available facilities and capabilities within the Member States. Such an infrastructure will support the remote testing of devices, enhance simulation capabilities for large multi-scale and multi-layer systems, while also achieving soft-sharing of expertise in a large knowledge-based virtual environment. Furthermore the platform should offer the possibility of keeping confidential all susceptible data/models/algorithms, enabling the participants to determine which specific data will be shared with other actors. This kind of simulation platform will benefit all actors that need to take decisions in the power system area. This includes national and local authorities, regulators, network operators and utilities, manufacturers, consumers/prosumers. The federation of labs is created through real-time remote access to high-performance computing, data infrastructure and hardware and software components (electrical, electronic, ICT) assured by the interconnection of different labs with a server-cloud architecture where the local computers or machines interact with other labs through dedicated VPN (Virtual Private Network) over the GEANT network (the pan-European research and education network that interconnects Europe’s National Research and Education Networks ). The local VPN servers bridge the local simulation platform at each site and the cloud ensuring the security of the data exchange while offering a better coordination of the communication and the multi-point connection. It is then possible the integration of the different sub-systems (distribution grid, transmission grid, generation, market, and consumer behaviour) with a holistic approach