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19 results for “package managers”
NeuroGentoo Presentation - Bringing the Power of Gentoo Package Management to (Neuro)Scientific Software Environments
<p>Neuroscience, one of the most computation-reliant fields in the natural sciences, is dependent upon dozens of highly complex software suites, which scientists are often forced to manage manually. Upstream developers often ship bundled dependencies to better support this flawed workflow, and in the resulting mess documenting and reproducing analysis pipelines is neigh-impossible. We seek to correct these shortcoming of both modern software distribution as well as modern data science, by integrating high-quality ebuilds for neuroscientific software into the Gentoo Science Overlay. We also seek to publish a simple NuroGentoo world file (along with appropriate usage instructions) to allow scientists with access to OpenStack, Amazon Elastic Computing, or Docker to launch up and build a system fit for reproducing data analysis run on other NeuroGentoo systems with minimum effort.</p>
LCC - Datasets for PlastiCircle Economic Evaluation of the Light Packaging Waste Management
<p>These datasets are related to deliverable 7.4 which presents the results from the economic assessment for the waste management systems evaluation of the city pilots and converter industries.</p> <p>PlastiCircle technologies allowed higher collection of plastic packaging and better input quality which might result in an increased market demand achieving the European recycling objectives. The higher volumes collected cause higher management costs which are compensated by higher remuneration from the PRO. The higher recovery resulted in higher recycling costs which are offset by higher revenues from sales. The evaluation of the industrial case studies indicates that the profitability of the production of industrial products using recycled post-consumer plastics is dependent of the market prices and the stability of the supply.</p>
S-LCA - Datasets for PlastiCircle Social Evaluation of the Light Packaging Waste Management (stakeholder perpectives)
<p>This deliverable (7.5) presents S-LCA results for the waste management systems in Valencia (Spain), Utrecht (Netherland) and Alba Iulia (Romania). Moreover, an extrapolation of the results has been calculated to show the results on the European level.</p> <p>Data is collected from groups of citizens, citizens associations, waste managers and municipalities (city hall workers).</p> <p>Questionnaires have been done in order to ask this groups of people questions to assess the social impact of the waste management systems in each city.</p> <p>Questions have been prepared according to the guidelines for social life cycle assessment of products - UNEP DTIE.</p>
LCA - Datasets for PlastiCircle Environmental Evaluation of the Light Packaging Waste Management
<p><strong>Datasets presented are related to Delievrable 7.3 - LCA.</strong></p> <p>To structure the environmental assessment, cities and industries are treated separately. The three pilot cities evaluated in PlastiCircle are:<br> 1. Valencia 2. Utrecht 3. Alba Iulia</p> <p><br> Within the project several technologies and strategies have been developed to improve the waste management of the pilot cities and other aspects in the value chain. Main developments spin around (a) citizens identification through a labelling system integrated on the container on a PAYT basis; (b) route optimization based on filling level sensors integrated on the container and corresponding algorithms for the optimization; (c) Eco-driving app to monitor the transport parameters and suggest efficient driving to the drivers. Moreover, on the sorting stage (d) PICVISA developed different improvement on their sorting equipment to enhance yield and purity on extracted fractions, and as well, a mechanical module to improve film recovery was developed. All these conditions have been evaluated on this report, focusing on the following sections:<br> 1. Collection – Impact of PAYT system on the pilot and quality segregation and IoT infrastructure.<br> 2. Transport – Impact of route optimization and efficient driving<br> 3. Sorting – Impact from yield and purity improvements and development of turbosorter</p> <p><br> For the sorting stage a model has been designed for each pilot waste recycling plant, focusing on the energy used per tonne, the yield of each fraction on the plant and the input waste as main parameters, inter alia.</p> <p>As a bridge between the cities and the final converters, there is a need of a proper pre-treatment/ conditioning of the post-consumer polymer to provide quality standard feedstock to the industries. PlastiCircle, lacks a partner on the consortium undertaking these tasks on an industrial level, however, different lab scale trials and some trials with Sorema have been assessed during the project, also considering broken down processes to evaluate impact on these intermediate stages taking into consideration processes such as the ones used by European industries as Sorema or Herbold, and other processes extracted from literature and commercial solutions:<br> 4. Washing/Pre-treatment – Impact on the pre-conditioning for plastic packaging bales.</p> <p><br> Once plastic is ready as flakes or pellets forms, industries within the project use those recycled plastic as raw materials to manufacture their products. Their different processes have been defined analysed, performing specific LCA for each one of their results considering the trials and impacts from the developments done during the project.</p> <p>Attached datasets used for the LCA models in PlastiCircle can be found.</p>
Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms
<p>This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections:</p> <ol> <li><strong>Virtual Machines (VMs): </strong>the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors. <br> - <em>Type 1 Hypervisor </em>runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, <strong>Microsoft Hyper-v</strong>, and Xen Project.<br> - <em>Type 2 Hypervisor</em> runs on an existing operating system, offering flexibility at the cost of some performance. Examples include <strong>Oracle VirtualBox</strong>, VMware Workstation, and Parallels.</li> <li><strong>Containers: </strong>the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like <strong>Docker</strong> and <strong>Kubernetes</strong> are shown as popular solutions for container orchestration.</li> <li><strong>Environment Management Packages: </strong>the top right section focuses on tools for managing software dependencies and environments. <strong>renv</strong> (for R) and <strong>Conda</strong> (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include <strong>Google Colab</strong>, <strong>Posit Cloud</strong>, JupyterHub, <strong>Binder</strong>, Nextjournal, OpenShift, and <strong>Code Ocean</strong>.</li> </ol> <p>Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.</p>
Assessing the sustainability performance of sustainability management software - replication package
<p>This is the replication package for the article "Assessing the sustainability performance of sustainability management software", published in Technologies – SI „Advanced Green Information and Communication Technology“</p> <p><strong>Contents</strong><br> The repository contains the following files:</p> <ul> <li><em>Data_-_Scenario_Hardware_Measurement.csv:</em><br> measurement data acquired during the measurement of the standard usage scenario</li> <li><em>Data_-_Scenario_Log.txt:</em><br> timestamp log file for the measurements of the standard usage scenario</li> <li><em>Data_-_Baseline_Hardware_Measurement.csv:</em><br> baseline meaurements</li> <li><em>Data_-_Baseline_Log.txt:</em><br> baseline timestamp log file</li> <li><em>R_image.Rdata:</em><br> data is also available in an R image dump</li> <li><em>R_Analysis_Script.R:</em><br> analysis script, written in R</li> <li><em>Results_SCSS_WeSustain_ESM.pdf:</em><br> results of the analysis</li> <li><em>Results_and_calculation_for_Indicators_1.2.b_and_1.2.c.ods:</em><br> calculation for indicators 1.1.4.d) 1.2.b) and 1.2.c)</li> <li><em>Usage-scenario_WeSustain_ESM.pdf:</em><br> description of the standard usage scenario</li> </ul> <p><strong>Usage</strong><br> To recreate the analysis, run the R script and, if necessary, modify <em>lines 103 to 115</em> to fit the filenames where to find the data.<br> <em>Lines 193 to 217</em> need to be executed manually, to generate the desired plots and calculations.</p> <p>It is also possible to load the <em>R_image.RData</em> data dump into an R session, import the library `psych` (<em>line 2</em> in the script) and manually execute <em>lines 193 to 217</em> to generate the desired plots and calculations.</p>
Replication package for: Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia
<p>This package contains the data and code needed to reproduce tables and figures in Iacovone, Maloney and McKenzie (forthcoming) "</p> <p><strong>Improving Management with Individual and Group-Based Consulting: Results from a Randomized Experiment in Colombia"., <em>Review of Economic Studies</em></strong>. </p> <p>Anonymized firm data are provided for analyzing impacts on management and employment. Authorized log files and output files are provided for analysis that uses the confidential EAM data, along with instructions for applying to access these data if required.</p>
Contrasting Third-Party Package Management User Experience
<p>The management of third-party package dependencies is crucial to most technology stacks, with package managers acting as brokers to ensure that a verified package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of package ecosystems with their own management features. While recent studies have shown that developers struggle to migrate their dependencies, the common assumption is that package ecosystems are used without any issue. In this study, we explore 13 package ecosystems to understand whether their features correlate with the experience of their users. By studying experience through the questions that developers ask on the question-and-answer site Stack Overflow, we find that developer questions are grouped into three themes (i.e., Package management, Input-Output, and Package Usage).<br> Our preliminary analysis indicates that specific features are correlated with the user experience. Our work lays out future directions to investigate the trade-offs involved in designing the ideal package ecosystem.</p>
PAReTT: a Python package for the Automated Retrieval and management of divergence time data from the TimeTree resource for downstream analyses (Dataset)
<p>Dataset for article by the same title submitted the the <em>Journal of Molecular Evolution</em>.</p>
The Brokers in a Dependency Ecosystem: An Empirical Study on using Package Managers
<p>A package manager (PM) is crucial to most technology stacks, acting as a broker to ensure that a verified dependency package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of PMs with various features. While recent studies have shown that developers struggle to migrate their dependencies, the common assumption is that PMs broker dependencies without any issues. In this paper, we would like to explore issues faced by developers when using PMs through an empirical study of content on Stack Overflow (SO). Through a quantitative analysis of 470,806 posts related to 16 PMs, we first determine the importance of PM-related posts and extract topics contained in these posts. We then carry out a qualitative analysis of 1,131 posts related to the Maven, npm, and NuGet dependency ecosystems to analyze questions, underlying causes and their resolutions. Our results confirm that developers tend to struggle with PMs. The choice of PM affects their user experience, as questions are likely to arise from lack of instructions and error messages. We find that PM-related posts are not likely to get resolved, with only 40% to 50% having an accepted answer. Our work further lays out a road-map to investigate the trade-off between design features to understand what an ideal PM would look like.</p>
An Empirical Study of Package Management Issues via Stack Overflow
<p>The package manager (PM) is crucial to most technology stacks, acting as a broker to ensure that a verified dependency package is correctly installed, configured, or removed from an application. Diversity in technology stacks has led to dozens of PMs with various features. While our recent study indicates that package management features of PM are related to end-user experiences, it is unclear what those issues are and what information is required to resolve them. In this paper, we have investigated PM issues faced by end-users through an empirical study of content on Stack Overflow (SO). We carried out a qualitative analysis of 1,131 questions and their accepted answer posts for three popular PMs (i.e., Maven, npm, and NuGet ) to identify issue types, underlying causes, and their resolutions. Our results confirmed that end-users struggle with PM tool usage (approximately 64-72%). We observed that most issues are raised by end-users due to a lack of instructions and errors messages from PM tools. In terms of issue resolution, we observed that external link sharing is the most common practice to resolve PM issues. Additionally, we found that links pointing to useful resources (i.e., official documentation websites, tutorials, etc.) are the most frequently shared, indicating the potential for tool support and the ability to provide relevant information.</p>
A Randomised Clinical Trial of a Digital Self-management Package for People With Interstitial Lung Disease
ClinicalTrials.gov study NCT06122233. IPD Sharing: YES. Countries: 1. Publications: 1.
Addition of Zinc to the Current Case Management Package of Diarrhea in a Primary Health Care Setting
ClinicalTrials.gov study NCT00278746. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Effectiveness of Adding Zinc to the Current Case Management Package of Diarrhea in a Primary Health Care Setting
ClinicalTrials.gov study NCT00278681. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Replication Package for Automating Technical Debt Management: Insights from Practitioner Discussions in Stack Exchange
Open the record for dataset details and reuse information.
Seizure Rescue Medication (RM) as Part of a Comprehensive Epilepsy Self-management Package of Care
ClinicalTrials.gov study NCT06346262. IPD Sharing: NO. Countries: 1. Publications: 0.
Benefits of a Household WASH Package to Community-based Management of Acute Malnutrition (CMAM) Program, Chad
ClinicalTrials.gov study NCT02486523. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Development and Validation of a Comprehensive Management Service Package for Older Adults With Multimorbidity
ClinicalTrials.gov study NCT07151430. IPD Sharing: NO. Countries: 0. Publications: 0.
Multimodal Pain Package vs. Regular Formulation for Pain Management in Ambulatory Spinal Surgery
ClinicalTrials.gov study NCT05965492. IPD Sharing: NO. Countries: 0. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.