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1,069 results for “Data Management”
Research Data Management Framework - POC-Study: Guideline and protocol
<p>Dataset for the following paper:<br><br>Proof-Of-Concept-Studie für das FDM in den Ingenieur:innenwissenschaften</p> <p><strong>Ein Forschungsdatenmanagement-Rahmenwerk</strong><br>T. Hamann, C. Florides, A. Abdelrazeq, R. H. Schmitt</p> <p>Forschungsdatenmanagement (FDM) gewinnt seit Jahren an Bedeutung. Das Ziel, Daten wiederverwendbar aufzubereiten und nachzunutzen anstatt sie aufwändig neu zu erheben, wird von Forschenden der deutschen Ingenieur:innenwissenschaften jedoch nur selten verfolgt. Um dem entgegenzuwirken, wurde ein Rahmenwerk für das FDM in den Ingenieur:innenwissenschaften entwickelt. In einer Proof-Of-Concept-Studie soll dieses nun erstmals anhand des Forschungsprojekts KIOptiPack validiert werden.</p> <p><strong>A Research Data Management Framework</strong></p> <p>Research data management (RDM) has been gaining in importance for years. However, the goal of preparing data sustainably and reusing existing data instead of laboriously collecting it from scratch is rarely pursued by researchers in the German engineering sciences. To counteract this, a framework for RDM in the engineering sciences was developed. In a proof-of-concept study, this framework will be validated for the first time using the research project KIOptiPack.<br>Stichwörter: Forschung, Informationsmanagement, Digitalisierung<br><br><br>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their funding and support within the framework of the NFDI4Ing consortium. Funded by the German Research Foundation (DFG) - project number 442146713.</p>
Data from: Integrated SDM database: Enhancing the relevance and utility of species distribution models in conservation management
<p><span>1. Species' ranges are changing at accelerating rates. Species distribution models (SDMs) are powerful tools that help rangers and decision-makers prepare for reintroductions, range shifts, reductions, and/or expansions by predicting habitat suitability across landscapes. Yet, range-expanding or -shifting species in particular face other challenges that traditional SDM procedures cannot quantify, due to large differences between a species' currently-occupied range and potential future range. The realism of SDMs is thus lost and not as useful for conservation management in practice. Here, we address these challenges with an extended assessment of habitat suitability through an <i>integrated SDM database (iSDMdb)</i>.</span></p> <p><span>2. The<i> iSDMdb</i> is a spatial database of predicted sites in a species' prediction range, derived from SDM results, and is a single spatial feature that contains additional, user-friendly data fields that synthesise and summarise SDM predictions and uncertainty, human impacts, restoration features, novel preferences in novel spaces, and management priorities. To illustrate its utility<i>,</i> we used the endangered New Zealand sea lion (<i>Phocarctos hookeri</i>). We consulted with wildlife rangers, decision-makers, and sea lion experts to supplement SDM predictions with additional, more realistic, and applicable information for management. </span></p> <p><span>3. Almost half the data fields included in this database resulted from engaging with these end-users during our study. The SDM found 395 predicted sites. However, the <i>iSDMdb</i>'s additional assessments showed that the actual suitability of most sites (90%) was questionable due to human impacts. >50% of sites contained unnatural barriers (fences, grazing grasslands), and 75% of sites had roads located within the species' range of inland movement. Just 5% of the predicted sites were mostly (>80%) protected.</span></p> <p><span>4. Integrating SDM results with supplemental assessments provides a way to address SDM limitations, especially for range-expanding or -shifting species. SDM products for conservation applications have been critiqued for lacking transparency and interpretation support, and ineffectively communicating uncertainty. The <i>iSDMdb</i> addresses these issues and enhances the practical relevance and utility of SDMs for stakeholders, rangers, and decision-makers. We exemplify how to build an <i>iSDMdb</i> using open-source tools, and how to make diverse, complex assessments more accessible for end-users.</span></p>
GloMPO (Globally Managed Parallel Optimization) Benchmark Test Data
<p>Dataset associated with:<br> M. Freitas Gustavo and T. Verstraelen (2021), "GloMPO (Globally Managed Parallel Optimization) - a tool for expensive, black-box optimizations: application to ReaxFF reparameterizations".</p> <p>Contains optimization trajectories using the CMA-ES optimizer applied to various benchmark functions and ReaxFF reparameterizations. Compares optimization results using the GloMPO framework (github.com/mfgustavo/glompo) to unmanaged optimization results.</p>
Parcel Manager data set
<p>Data contained in this repository can be used for replicating the two use cases of Parcel Manager software application presented in the paper (Colomb et al., 2021, submitted).</p> <p>The first use case enables the simulation of a scenario called <em>test scenario</em> designed specifically to test different parcel division processes and workflows with Parcel Manager. The area under study is a small community (Gennes, 681 inhabitants) located in the east of France. Data used are from the French IGN BD Topo 2018.</p> <p>The second use case enables the comparison of the shape of parcels created by simulation with the shape of parcels in real cases. The comparison concerns the parcel plans in 2003 and 2018 of 11 communities of the Seine-et-Marne department, near Paris capital city (France). Data for 2018 are from the French IGN BD Topo 2018. They have been cleaned manually as follows:</p> <ul> <li>removing parcels that represent driveways or roads which could be used by residential parcels, – removing the tiny parcels resulting from a former division process and that could prevent the access to roads of neighbouring parcels,</li> <li>removing parcels located on water surfaces or railways,</li> <li>removing roads of type ’trails’ and ’stairs’.</li> </ul> <p>Parcel data for 2003 are from the IGN BD-Parcellaire 2003. Building and road data come from the IGN BD-Topo 2005. The cleaning results of the 2018 parcel plan have been copied into the 2003 parcel plan.</p> <p>The road network encompasses every types of paths that are accessible with a car.</p> <ul> <li>Driveways are small paths for cars that connect a house to the road network. Driveways are included in parcels and are not considered as a proper road.</li> <li>Gravel roads are non-asphalted trails. They can occasionally be used by cars. Their level of attraction is low (level 2).</li> <li>Lanes are small roads dedicated to house access. Their level of attraction is maximal (level 4). Note that peripheral roads created with the straight skeleton algorithm are classified as lanes.</li> <li>Streets are roads that connect lanes. Their level of attraction is high (level 3).</li> <li>Arterial roads are high-speed roads connecting communities. Their level of attraction for parcel contact is minimal (level 1).</li> </ul> <p>In the data set created for the <em>t</em><em>est Scenario</em>, the road layer has been manually enriched: missing trails and lanes have been added; type and level of attraction of some road segments have been changed.</p>
Data from: A burning issue: Savanna fire management can generate enough carbon revenue to help restore Africa's rangelands and fill Protected Area funding gaps
<p>Many savanna-dependent species in Africa including large herbivores and apex predators are at increasing risk of extinction. Achieving effective management of protected areas (PAs) in Africa where lions live will cost an estimated USD >$1-2 B/year in new funding. We explored the potential for fire management-based carbon-financing programs to fill this funding gap and benefit degrading savanna ecosystems. We demonstrated how introducing early dry season fire management programs could produce potential carbon revenues (PCR) from either a single carbon-financing method (avoided emissions) or from multiple sequestration methods ranging from USD $59.6-$655.9 M/year (at USD $5/ton) or USD $155.0 M–$1.7 B/year (at USD $13/ton). We highlighted variable but significant PCR for savanna PAs from USD $1.5–$44.4 M/year per PA. We suggest investing in fire management programs to jump-start the United Nations Decade of Ecological Restoration to help restore degraded African savannas and conserve imperiled keystone herbivores and apex predators. <br> <br> Open Access article: <a href="https://doi.org/10.1016/j.oneear.2021.11.013">https://doi.org/10.1016/j.oneear.2021.11.013</a></p>
Data from: Drought and recovery effects on belowground respiration dynamics and the partitioning of recent carbon in managed and abandoned grassland
<p>The supply of soil respiration with recent photoassimilates is an important and fast pathway for respiratory loss of carbon (C). To date it is unknown how drought and land-use change interactively influence the dynamics of recent C in soil respired CO<sub>2</sub>. In an <em>in situ </em>common-garden experiment, we exposed soil-vegetation monoliths from a managed and a nearby abandoned mountain grassland to an experimental drought. Based on two <sup>13</sup>CO<sub>2</sub> pulse-labelling campaigns, we traced recently assimilated C in soil respiration during drought, rewetting and early recovery. Independent of grassland management, drought reduced the absolute allocation of recent C to soil respiration. Rewetting triggered a respiration pulse, which was strongly fueled by C assimilated during drought. In comparison to the managed grassland, the abandoned grassland partitioned more recent C to belowground respiration than to root C storage under ample water supply. Interestingly, this pattern was reversed under drought. We suggest that these different response patterns reflect strategies of the managed and the abandoned grassland to enhance their respective resilience to drought, by fostering their resistance and recovery, respectively. We conclude that while severe drought can override the effects of abandonment of grassland management on the respiratory dynamics of recent C, abandonment alters strategies of belowground assimilate investment, with consequences for soil-CO<sub>2</sub> fluxes during drought and drought-recovery.</p>
Data from Finnish Research Data Management Training Survey 2020-2021
<p>This is the survey data used in The Finnish Research Data Management Training Survey 2020-2021. The survey was sent to 74 Finnish research organizations of which 36 responded. The aim of the report was to gain a deeper understanding of what kind of research data management (RDM) training activities are provided by different Finnish organizations.</p> <p> </p>
Survey used and data gathered for research into adoption of carbon management strategies amongst universities E Lewis-Brown et al 2022
<p>Survey used and data gathered for research into adoption of carbon management strategies amongst universities 2022, which forms part of a PhD thesis and will be submitted for publication in a journal. </p>
Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey - Extended Data 4 - Analysis Data Sheet
<p>This dataset is extended data to the manuscript "Research data management for bioimaging: the 2021 NFDI4BIOIMAGE community survey" by Schmidt C., Hanne J, Moore J, Meesters C, Ferrando-May E, Weidtkamp-Peters S, and members of the NFDI4BIOIMAGE initiative. [version 1; peer review: awaiting peer review] F1000Research 2022, 11:638, https://doi.org/10.12688/f1000research.121714.1</p> <p>This extended data includes:</p> <p>- Data Analysis Sheet and results table</p> <p>Note: The data is anonymized (i.e., all IP addresses as well as personal comments were deleted)</p> <p>The revised version was published after the peer-review process of the original article on zenodo.org</p>
Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)
<p>This video is the fourth talk from our Future Blood Testing Network Plus Launch that took place on the 23/11/2021.</p> <p>Network Theme: Can blood sampling become a new data source in the role of self-monitoring and self-management of health? - Dr Mark Elliott (University of Warwick)</p> <p>Bio: <strong><a href="https://warwick.ac.uk/fac/sci/wmg/people/profile/?wmgid=1147">Dr Mark Elliott</a> </strong>Mark is an Associate Professor at the Institute of Digital Healthcare, WMG, University of Warwick (UoW). Mark’s core research focuses on human movement and physiology analytics. His research uses signal processing and data science approaches to monitor, measure and model human movement and physiology to infer health status. He is the PI of the WMG Motion Capture Laboratory. His work further extends into the broader area of using wearable and on-the- body sensing devices to make objective measures of human behaviour and behaviour change. Much of Dr Elliott’s research is highly applied and involves collaborating with commercial and NHS partners. He has received funding from EPSRC, Innovate UK and SBRI Healthcare, as well as direct industrial funding. He is currently Data Analytics Theme Lead for the EPSRC funded OATech+ Network and on the steering committee for the EPSRC funded VSimulators facilities at Bath and Exeter.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/23-11-21-future-blood-testing-network-launch/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/ChdbggScUgo</p>
Data from "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction"
<p>Sediment data from Steart Marshes described in Mossman et al. "Rapid carbon accumulation at a saltmarsh restored by managed realignment exceeded carbon emitted in direct site construction".</p> <p>Data are provided as a .xlsx file (Data package.xlsx) with four tabs. Tab 1 has column heading descriptions. Tab 2 has total carbon samples. Tab 3 has total organic carbon samples. Tab 4 has bulk density samples. Each tab is also provided as a seperate csv file.</p>
Research Data Management: Data Lifecycle
<p>This diagram has been created by the team of data steward at the University of Bologna (Alma Mater Studiorum - Università di Bologna, UniBo) in October 2022. It proposes a data lifecycle model inspired by the University of Virginia Library’s model (<a href="https://guides.lib.virginia.edu/c.php?g=515290&p=3522215">https://guides.lib.virginia.edu/c.php?g=515290&p=3522215</a>). It has been developed in parallel to the Research Data Management Decision Tree, available here: <a href="https://doi.org/10.5281/zenodo.7190004">https://doi.org/10.5281/zenodo.7190004</a></p> <p>Emphasis is put on a careful planning of data management, which should always precede data collection (and re-use of existing data). A reference to the opportunity of creating and maintaining a Data Management Plan (DMP) has been added. This is not always compulsory, but is increasingly required by funders.</p> <p>Collecting, analysing and storing data (and possibly sharing them with a group) remain at the heart of the lifecycle, constituting what we called “data handling”. Here, the process is not linear, and researchers tend to move from one stage to another in a recursive fashion. </p> <p>At any point during data handling, it is possible to deposit data: the responsibility for storing and safekeeping is passed on to the repository, the time-scale shifts from short-term to long-term, and data become citable (and possibly discoverable) by the wider scholarly community and beyond. Importantly, deposited data can always be re-used as the basis for a new round of collection/analysis/storage that will lead to a new deposit, and so on.</p>
Data set on soil physicochemical parameters, biomass accumulation and carbon credit generation in different management systems in Rio Verde, GO, Brazil
<h1>Description</h1> <p>This repository contains a comprehensive dataset focused on soil organic carbon and its role in mitigating climate change through carbon sequestration on agricultural lands in Rio Verde, GO, Brazil. With the global imperative to reduce anthropogenic CO2 emissions, our data highlights the effectiveness of no-till agricultural practices in both improving soil quality and enhancing carbon storage. This collection represents extensive soil and biomass sampling from five distinct areas within the Cerrado region, utilizing three priority management systems:</p> <p>No-till with soybean and maize in sequence under rainfed conditions. No-till with soybean and maize in sequence with central pivot irrigation. First and second cuts of sugarcane. The samples were meticulously collected post-harvest and used to estimate both soil biomass accumulation and carbon stock indices. A thorough analysis of the soil's physicochemical parameters was conducted for the 0-20 cm soil profile in each area. This dataset not only provides a valuable resource for studying the impact of different no-till practices on carbon sequestration but also serves as a critical input for modeling future contributions of conservation management systems to carbon trading markets.</p> <div> <div> </div> <div> <h2>Data Contents</h2> </div> <p>Soil organic carbon measurements for various no-till systems. Biomass accumulation data post-harvest. Carbon stock indices derived from biomass samples. Detailed physicochemical profiles of soil samples.</p> <div> <h2>Significance</h2> </div> <p>This dataset is pivotal for researchers and policymakers focusing on the potentials of agricultural carbon sequestration and its implications for carbon trading schemes. It offers insights into the current contributions of no-till conservation management systems and aids in the development of future strategies to enhance carbon</p> <h1>Metadata Description and Script</h1> </div> <p>This repository contains two key data files that encapsulate diverse aspects of soil physicochemical parameters, biomass accumulation, and carbon credit generation across different management systems in Rio Verde, GO, Brazil. Below are descriptions of each file's contents and structure.</p> <div> <h2>all.txt</h2> </div> <p>This text file presents aggregated data from various sites under different agricultural management systems. Each row in the dataset represents measurements from distinct sample plots, with the following fields:</p> <ul> <li><code>Sites</code> - Identifier for the plot location.</li> <li><code>SB</code> - Soil bulk density (g/cm³).</li> <li><code>SOC</code> - Soil organic carbon (%).</li> <li><code>Stock</code> - Carbon stock (ton/ha).</li> <li><code>Biomass</code> - Biomass accumulation (ton/ha).</li> <li><code>Credits</code> - Estimated carbon credits (ton CO2 equivalent/ha).</li> </ul> <div> <h2>Quimica.xlsx</h2> </div> <p>This Excel file provides detailed physicochemical analyses of soil samples from different management zones in the study area. The data is structured to support in-depth analysis of soil characteristics influencing carbon sequestration capabilities. Each sheet in the workbook corresponds to a specific area, with columns typically representing:</p> <ul> <li><code>pH</code> - Soil pH, indicating the acidity or alkalinity.</li> <li><code>EC</code> - Electrical conductivity (dS/m).</li> <li><code>Cation Exchange Capacity (CEC):</code> - (meq/100g).</li> <li><code>Organipont c Matter:</code> - (%).</li> <li><code>NPK levels</code> - Concentrations of Nitrogen (N), Phosphorus (P), and Potassium (K).</li> </ul>
Data from: Growth and longevity of the endangered freshwater pearl mussel (Margaritifera margaritifera): Implications for conservation and management
<p>Key life-history data, such as growth and age, are necessary to effectively manage and conserve threatened freshwater mussel species. Traditionally growth and age studies require large yet destructive sample sizes covering all age classes. Such methods pose a risk to populations of conservation concern, and therefore alternative methods that need only limited sample sizes are necessitated to prevent further threats to such populations. We applied retrospective shell growth at age reconstructions to 98 critically endangered freshwater pearl mussel (FPM) individuals from 34 populations across Finland and Sweden, enabling the use of extremely small sample sizes (n = 1–6 per population). We compared the performance of six different growth models with the reconstructed size-at-age data across FPM juvenile (<20 years old) and adult life stages. The growth reconstruction model showed reasonable skill in reconstructing FPM growth patterns. The von Bertalanffy model was shown to be a good general descriptor of growth for FPM, but it systematically underestimated the asymptotic size. The power law model was the most accurate in estimating juvenile growth (lowest deviances from the size-at-age data). FPM showed great variability in longevity (A<sub>max</sub> = 54–254 years) and growth constant k (0.018– 0.057 year<sup>-1</sup>). Our results show that reasonable estimates of growth can be attained even when sample sizes are extremely limited. The results can be further applied to gain knowledge on the population's age structure, size at maturation, and recovery potential. The methodology is applicable to other freshwater mussel species of conservation concern.</p>
Dataset for: Developing research data management services and support for researchers: a mixed methods study
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manuscript: <br> Perrier L, Barnes L. Developing research data management services and support for researchers: a mixed methods study. Partnership. 2018;13(1). doi: doi.org/10.21083/partnership.v13i1.4115.</p> <p>Full-text available at: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202</a></p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset: </p> <ol> <li>Coding Scheme: RDMServicesSupport_Codes.txt</li> <li>Transcript, Focus Group 01 (anonymized): RDMServicesSupport_FocusGroup01.pdf</li> <li>Transcript, Focus Group 02 (anonymized): RDMServicesSupport_FocusGroup02.pdf</li> <li>Transcript, Focus Group 03 (anonymized): RDMServicesSupport_FocusGroup03.pdf</li> <li>Transcript, Focus Group 04 (anonymized): RDMServicesSupport_FocusGroup04.pdf</li> </ol> <p>Contact: Laure Perrier: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">orcid.org/0000-0001-9941-7129</a></p>
RAGE pilot data from 1st evaluation of the Sports Team Manager game on soft skills for employability
<p><strong>General description: </strong>The dataset includes data from the first 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: May-June 2017</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<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>: D8.3 First RAGE Evaluation Report<br> Related dataset: <a href="https://doi.org/10.5281/zenodo.2564742">10.5281/zenodo.2564742</a></p>
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>
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>
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>
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>
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.