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13,276 results for “IT management”
RAGE - Data Evaluation Taxonomy Manager
<p><strong>General description: </strong>Data from the evaluation of the Taxonomy Manager in the RAGE Ecosystem</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human-centered computing - Human computer interaction (HCI) - User studies, Usability testing</p> <p><strong>Name entitites</strong><br> Organizational information: Graz University of Technology, Forschungsinstitut für Telekommunikation und Kooperation<br> Geographical information: Europe<br> Time information: 2017</p> <p><strong>Types</strong>: SPSS data file</p> <p><strong>RAGCS target group:</strong> target groups - supply side - research institutions, industry participants</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Taxonomy Manager in RAGE Ecosystem portal<br> Evaluation variables: usability, usefulness, tutorial quality, experience with taxonomies, version control, export/import quality</p> <p><strong>Instruments:</strong> UMUX - usability metric for user experience (Finstad, 2010); USE questionnaire - Usefulness, Satisfaction, Ease of use; subscale usefulness; questionnaire items adapted from Papadakis, Andreou & Chrissykopoulus (2002), Thielsch & Stegemöller (2012); questionnaire items defined for the purpose of the evaluation<br> <br> <strong>Relationships</strong>: D8.3 - First RAGE Evaluation Report<br> Related datasets: <a href="https://doi.org/10.5281/zenodo.1209192">10.5281/zenodo.1209192</a>, <a href="https://doi.org/10.5281/zenodo.2578728">10.5281/zenodo.2578728</a></p>
Results from Performance Evaluation and Testing of Virtual Infrastructure Managers
<p>NFV leverages Cloud Computing principles to move the data-plane network functions from expensive, closed and proprietary hardware to so-called Virtual Network Functions (VNFs). We deal with the management of virtual computing resources (Unikernels) for the execution of VNFs. This functionality is performed by the Virtual Infrastructure Manager (VIM) in the NFV MANagement and Orchestration (MANO) reference architecture. In this data set we report the results of a performance evaluation we have realized of three open source VIMs, namely OpenStack, Nomad and OpenVIM; both considering stock and the tuned versions. The VIMs and the performance evaluation tools that we employ are provided openly and can be downloaded from our repositories (<a href="https://github.com/superfluidity/openvim4unikernels">https://github.com/superfluidity/openvim4unikernels</a> and <a href="https://github.com/netgroup/vim-tuning-and-eval-tools">https://github.com/netgroup/vim-tuning-and-eval-tools</a>).</p>
Investigating Data Assets, Management, and Planning at UF
<p>This dataset represents a data assessment of select researchers across multiple communities of practice at the University of Florida as part of an IRB 201602303 study to investigate the data management practices, storage, and training needs of researchers. The study was conducted from January 3, 2017 - April 30, 2017. One hundred fifty-nine starts, one hundred fifty-six informed consent, and one hundred thirty-three completes for a 83% completion. However, Question 26 which contained PID was deleted from this raw dataset.</p>
MADFORWATER: WP3: Adaptation of technologies for efficient water management and treated wastewater reuse in agriculture: Task3.1: Reduction of crop water requirement and tools for irrigation management with treated WW: Subtask 3.1.1: Plant Growth Promotion (PGP) bacteria to enhance crop resistance to water stress and salinity: Subset1
<p>This dataset contains the data underlying the following publication: Mouna Mahjoubi, Simone Cappello, Yasmine Souissi, Atef Jaouani and Ameur Cherif (February 7th 2018). Microbial Bioremediation of Petroleum Hydrocarbon– Contaminated Marine Environments, Recent Insights in Petroleum Science and Engineering Mansoor Zoveidavianpoor, IntechOpen, DOI: 10.5772/intechopen.72207</p> <p> </p>
Cloud management platform evaluation data generated by CMP²
<p>This repository contains exemplary results from using the CMP² (Comparing Cloud Management Platforms) testbed on CloudcheckR, ManageIQ, MistIO, Boto and Libcloud. The results give insight into the performance of multi-cloud middleware. All experiments were conducted as research in education linked to the Cloud Accounting and Billing research initiative at Service Prototyping Lab, Zurich University of Applied Sciences, Switzerland. Apart from the raw data in JSON format, generated graphs are also included.</p> <p> </p>
Effects of community management on user activity in online communities
<p>Data and code needed to reproduce the results of the paper "Effects of community management on user activity in online communities", available in draft <a href="https://www.overleaf.com/13551865bzgswwbvkpgq#/52358582/">here</a>.</p> <p>Instructions:</p> <ol> <li>Unzip the files.</li> <li>Start with JSON files obtained from calling platform APIs: each dataset consists of one file for posts, one for comments, one for users. In the paper we use two datasets, one referring Edgeryders, the other to Matera 2019.</li> <li>Run them through edgesense (https://github.com/edgeryders/edgesense). Edgesense allows to set the length of the observation period. We set it to 1 week and 1 day for Edgeryders data, and to 1 day for Matera 2019 data. Edgesense stores its results in a file called JSON network.min.json, which we then rename to keep track of the data source and observation length.</li> <li>Launch Jupyter Notebook and run the notebook provided to convert the network.min.json files into CSV flat files, one for each netwrk file</li> <li>Launch Stata and open each flat csv files with it, then save it in Stata format.</li> <li>Use the provided Stata .do scripts to replicate results.</li> </ol> <p>Please note: I use both Stata and Jupyter Notebook interactively, running a block with a few lines of code at a time. Expect to have to change directories, file names etc.</p> <p> </p>
Final Report on Data Management - Raw data of DC-TRNG for D2.4 statistical testing
<p>Collected Raw & Post-processed data from DC-TRNG for both AIS-31 and NIST800-90B tests suites for Final Report on Data Management</p> <p>The purpose of the final report on data management is to provide an update of the analysis of the main elements of the data management policy used by the applications with regards to all the datasets that were generated by the project. Most important aspects regarding data management, like metadata generation, data preservation, and responsibilities, were updated compared to the initial report D5.2 (Data Management Plan) according to the outcome of the project.</p>
Dataset for "Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes"
<p>Reproducible dataset for "Comparison of the Fast and Slow Climate Response to Three Radiation Management Geoengineering Schemes"</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>
RAGE pilot data from 2nd evaluation of the Sports Team Manager game on soft skills for employability
<p><strong>General description: </strong>The dataset includes data from the second evaluation pilot which tested the Sports team manager game developed by PlayGen for the Okkam use case.</p> <p><strong>Topic</strong><br> ACM CSS 2012: Human Computer Interaction (HCI) design and evaluation methods<br> PsycINFO Classification: 3620 Personnel Management & Selection & Training; 2228 Occupational & Employment Testing</p> <p><strong>Name entitites</strong><br> Organizational information: OKKAM, in collaboration with University of Trento<br> Geographical information: Italy<br> Time information: December 2017- November 2018</p> <p><strong>Types</strong>: Excel</p> <p><strong>RAGCS target group:</strong> end users: other user groups</p> <p><strong>Evaluation dimensions</strong><br> Evaluation object: Sports Team Manager game<br> Methodology/design: within subjects design for learning<br> Evaluation variables: usability, user experience, learning</p> <p><strong>Instruments:</strong> 1. Questionnaire on Usability Game User Experience Satisfaction Scale (GUESS; Phan, Keebler, & Chaparro, 2016) – Usability subscale; 2. questionnaire on User Experience including 3 subscales: Enjoyment (GUESS -Enjoyment subscale); Usefulness (Intrinsic Motivation Questionnaire, IMI; Ryan, 1982) - Subscale Value/Usefulness; Flow (Flow Short Scale, FSS, Rheinberg et al., 2003; Vollmeyer & Rheinberg, 2006); 3. Pre-post questionnaire on learning; 4. Focus interview</p> <p><strong>Knowledge/skill elements</strong><br> RAGCS skills: cognitive skills: evaluating, analysing; affective skills: interpersonal skills<br> ESCO skills: social interaction: <a href="http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330">http://data.europa.eu/esco/skill/8f18f987-33e2-4228-9efb-65de25d03330</a>; accept constructive criticism: <a href="http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30">http://data.europa.eu/esco/skill/a311ab20-75df-4aff-8016-3142c5659d30</a>; work in teams: <a href="http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0">http://data.europa.eu/esco/skill/60c78287-22eb-4103-9c8c-28deaa460da0</a>; negotiate compromise: <a href="http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74">http://data.europa.eu/esco/skill/7954861c-86d4-4529-afbb-2c23dab9ac74</a>; lead others: <a href="http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207">http://data.europa.eu/esco/skill/75d8e5d9-bef3-418b-9011-01bff9f27207</a>; motivate others: <a href="http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507">http://data.europa.eu/esco/skill/e2d44a9b-f28c-489e-9861-b654b5ded507</a>; support colleagues: <a href="http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224">http://data.europa.eu/esco/skill/95a41cf5-4037-4c96-91a8-c34b41637224</a>; manage time: <a href="http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8">http://data.europa.eu/esco/skill/d9013e0e-e937-43d5-ab71-0e917ee882b8</a>; make decisions: <a href="http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47">http://data.europa.eu/esco/skill/d62d2b4c-a6f8-439e-8a1b-4f29ab5f2c47</a>; develop strategies to solve problems: <a href="http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800">http://data.europa.eu/esco/skill/7a8fb784-67fa-41e9-a75c-6b491d91f800</a>; evaluate information: <a href="http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9">http://data.europa.eu/esco/skill/7dd94ad3-13d6-43fe-8b94-51fcbf67ced9</a><br> <br> <strong>Relationships</strong>: D8.4 Second RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.1209206">10.5281/zenodo.1209206</a></p>
Guidelines for Data Management Plan implementation of One Health EJP projects: Webinar held on the 19th December 2018
<p>Webinar held on the 19th December 2018 to introduce Data Management Plan to scientists involved in the research and integrative projects of One Health European Joint Program.</p>
Response of wild bee diversity, abundance and functional traits to vineyard inter-row management intensity and landscape diversity across Europe
<p>Data set used for analyses in the publication "Response of wild bee diversity, abundance and functional traits to vineyard inter-row management intensity and landscape diversity across Europe".</p> <p>First sheet in the Excel-file gives a detailed description of the abbreviations, terms etc. used in the following tables. Please also check the method section in the publication for the detailed description on how data were collected.</p> <p>If you have any questions feel free to contact Sophie Kratschmer via e-mail</p>
Illustrative Darwin core archive to input data on a citizen science platform from a collection management system
<p>Illustrative DwC archive to send data from a collection management system to a citizen sciences platform. This illustrative archive displays the specimens used for the trans-institutional and trans-platform pilot project held in the frame of ICEDIG.</p> <p>Further description of its content in the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a citizen science platformback to a collection management system, multi-determined case
<p>Illustrative example of data format following Darwin Core sent back from a citizen science platform to the relevant collection management system. Multi-determined herbarium sheet case : http://coldb.mnhn.fr/catalognumber/mnhn/p/p01978557</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a citizen science platformback to a collection management system, simple case
<p>Illustrative example of data format following Darwin Core sent back from a citizen science platform to the relevant collection management system. Simple case : http://coldb.mnhn.fr/catalognumber/mnhn/p/p03558024</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a collection management system to a citizen science platform, multi-imaged case
<p>Illustrative example of data format following Darwin Core to send from a collection management system to a citizen science platform. Multi-imaged vertebrate specimen case : http://coldb.mnhn.fr/catalognumber/mnhn/zo/2013-152</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
Example structure of data sent from a collection management system to a citizen science platform, simple case
<p>Illustrative example of data format following Darwin Core to send from a collection management system to a citizen science platform. Simple case : http://coldb.mnhn.fr/catalognumber/mnhn/p/p03558024</p> <p>Illustration of the milestone28 document, worpackage 5.2 of the ICEDIG project.</p>
LIBER 2019 Workshop. Open Access books in academic libraries – how can we adapt workflows and cost management to an open scholarly communications landscape?
<p>This dataset includes all the results from a workshop held at the LIBER Annual Conference 2019 on June 26, 2019, in Dublin, Ireland. The workshop aimed at collecting and discussing current library practices related to open access books.</p> <p>The dataset includes information from a survey made in preparation for the conference, where 67 European libraries responded to a questionnaire based on activities or workflows in libraries related to open access books. Both the survey questionnaire and the results from the survey are uploaded as separate files.</p> <p>The dataset also includes the presentation made by keynote speaker Eelco Ferwerda from the OAPEN Foundation. He presented results based on the 2017 landscape study report on open access monographs with some new results from more recent studies by Springer Nature and a follow-up report by Knowledge Exchange.</p> <p>Olaf Siegert from ZBW - Leibniz Information Centre for Economics presented a brief overview of what libraries can do to promote OA books in terms of collection management, publication services and development of staff and organisation. The conclusion is that it is not necessarily big changes that are needed.</p> <p>Sofie Wennström presented results from a survey aimed at European research libraries on behalf of the LIBER Open Access Working Group. The survey reveals that many libraries are already working with processes to promote OA books. This is done by libraries organising publishing services or inhouse publishing, by including OA books in discovery services and repositories and by supporting authors to learn more about open access and open licensing.</p> <p>Finally, the LIBER Open Access Working Group shares a report from the workshop providing some quick takeaways and some good examples brought up during the breakout session with the workshop participants.</p>
Data from: Spatial configuration matters when removing windfelled trees to manage bark beetle disturbances in Central European forest landscapes
<p>The published dataset contains the result of the paper titled <strong>Spatial configuration matters when removing windfelled trees to manage bark beetle disturbances in Central European forest landscapes, in Journal of Environmental Management.</strong></p> <p>Two zip files contain maps in ascii format for the total bark beetle and wind damage over 54 simulation year in our study region in Slovakia under reference climate and different climate change scenarios. No- salvaging and 95% salvaging scenarios are shown, just as in the paper.</p> <p>Excel file contains data of the other figures in the paper (main text and appendices as well).</p> <p>See more details about target area, and iLand model:</p> <p>https://www.sciencedirect.com/science/article/pii/S0168192318302946</p> <p>http://iland.boku.ac.at/startpage</p> <p>contact: Laura Dobor; dobor.laura@gmail.com</p> <p> </p>
Identifying and Implementing Relevant Research Data Management Services for the Library at the University of Dodoma, Tanzania
<p>This data set presents the results of research conducted at the University of Dodoma, Tanzania. The purpose of the research was to identify and report on relevant RDM services that need to be implemented so that researchers and university management could collaborate and make our research data accessible to the international community.</p> <p>The data set was used to support both the mini-dissertation as well as a paper published in the Data Science Journal. The journal paper presents findings on important issues for consideration when planning to develop and implement RDM services at a developing country, academic institution. The paper also mentions the requirements for the sustainability of these initiatives.</p>
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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.