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240 results for “survey results”
Results from the RDM Survey - LEARN project (June 2016)
<p>First data obtained from the open survey developed by the LEARN project (http://www.learn-rdm.eu/) as a self-assessment tool to assist institutions discover how ready they are for managing research data. The survey is based on the issues posed to institutions by the LERU Roadmap for Research Data published at the end of 2013, and available at: http://www.learn-rdm.eu/material/leru_roadmap_for_research_data<br /> The survey has thirteen questions addressing the main elements to be taken into account in developing an institutional strategy for research data management. Each question has three possible answers representing green, yellow or red light. The more ‘green light’ responses recorded, the readier an institution probably is for managing its research data.</p> <p>The survey is available in English at http://learn-rdm.eu/en/rdm-readiness-survey/ and in Spanish at http://learn-rdm.eu/encuesta-rdm/</p>
Survey Results - User Accuracy Effects on Algorithmic Accuracy
<p>The survey was hosted on Qualtrics and participants recruited via Cloud Research. Participants are US-only. The data includes those who did not finish. No PII data was collected. </p><p>The survey included a deception scenario for a mortgage application followed by a battery of questions to assess ratings of the algorithm, assess participant honesty, and assess algorithmic awareness.</p>
External Stakeholders Survey Results - The Future of Aquaculture The impact of 4.0 technologies worldwide
<p>Aquaculture 4.0 technologies have landed and are very likely to stay, aiming to play a major role within the implementation of new Circular Bioeconomy approaches. In this context, European aquaculture has been recently applying innovative and disruptive technologies to transform fishery management strategies. The so-called “4th industrial revolution” is projected to allow a 15-20% increase in the sector by the year 2030. In addition to the growth the revolution can provide, the benefits of Industry 4.0 include improved productivity, efficiency and reduced costs. Companies will be able to produce more, in less time, while allocating resources more effectively, due to a smooth adoption of interconnectivity through the Internet of Things (IoT), access to real-time data, and the introduction of cyber-physical systems. According to FAO data, the estimated production volume of fish from European aquaculture in 2028 will increase to approximately 1.4 million tons, needing more circular, digitized solutions to cover end user demand.</p> <p>This data was collected from a survey investigating the Future of Aquaculture The impact of 4.0 technologies worldwide. The results of this survey were used to understand the main challenges faced within the Aquaculture 4.0 market concerning usage experience and level of awareness, in order to identify barriers for implementation and key drivers to encourage adoption of innovative technologies within their businesses. The insights gathered will help us to improve our concept and ultimately the whole value chain of the Aquaculture 4.0 market.</p>
Survey results - Implementation of interprofessional collaboration
<p>This dataset contains the variables, values and results for a survey run among the students of the pilot course "Interprofessionelle Gesundheitsversorgung - online" to determine effects of the course on the implementation of interprofessional collaboration in students' professional environment.</p> <p>The dataset is licensed under a Creative Commons Attribution 4.0 International (<a href="https://creativecommons.org/licenses/by/4.0/legalcode">CC BY 4.0</a>) license.</p>
Which journal characteristics are crucial for scientists when selecting journals for their publications? Results tables of an online survey
<p>As part of the BMBF-funded project "B!SON - Bibliometric and Semantic Open Access Recommender Network", an online survey was conducted among scientists using SoSci Survey (Leiner, 2019). The aim of the survey was to determine the importance of various characteristics of scientific journals in the decision for a publication venue by scientists. The characteristics were determined by analysis of other recommender systems, literature research and discussion with scientists. The data published here are based on 884 completed questionnaires (only questionnaires in which at least 90% of the questions had been answered were included in the analysis).</p> <p>In the questionnaire, a distinction was made between those characteristics that scientists would like to use to limit the selection of eligible journals from the outset (table "B!SON_Survey_Filter_Criteria_EN") and those that scientists need for their final decision from a list of recommended journals ("B!SON_Survey_Journal_Selection_EN"). For each journal property, the respondents could choose between the categories of a 5-point Likert scale: "not at all important - not very important - somewhat important - very important - extremely important". If the scientists were not able to evaluate a characteristic, they could also select "I can't say". </p> <p>In the two tables of results, the approval percentage and the rank based on it are listed for all journal properties queried in the respective part of the survey. The approval percentage is calculated from the percentage of people who rated the respective property as "very important" or "extremely important". Approval rate and rank are presented across all respondents (overall column), as well as within the 4 science disciplines by DFG (Natural Sciences, Engineering Sciences, Life Sciences, Humanities and Social Sciences) and the category "Other Sciences".</p> <p>Both tables can be sorted by approval rate and rank per scientific discipline or across disciplines.</p> <p>Note on the use of the HTML files: These can be downloaded via the download button and then opened and viewed with any web browser.</p> <p>A German version of the results tables is available under <a href="https://doi.org/10.5281/zenodo.5412197">https://doi.org/10.5281/zenodo.5412197</a>.</p>
Results of EUA survey on universities and innovation
<p>This database refers to the data collected by the European University Association (EUA) through its survey on universities and innovation in 2021. Designed to gather evidence about the state of innovation at European universities, the EUA survey took stock of how these institutions pursue their third mission and help deliver the sustainable and digital transitions. As such, it continues EUA’s long-standing work showcasing universities’ key contributions to innovation ecosystems, in a context of multiplying societal challenges and the increasing relevance of knowledge to devising new solutions. The full report published by the Association is available at: <a href="https://www.eua.eu/resources/publications/1014:universities-as-key-drivers-of-sustainable-innovation-ecosystems.html">https://www.eua.eu/resources/publications/1014:universities-as-key-drivers-of-sustainable-innovation-ecosystems.html </a></p> <p>All information that could lead to the identification of individual universities and other higher education institutions was removed from the database. The following files are available:</p> <ul> <li>EUA survey on universities and innovation</li> <li>Database in the following formats: .xlsx (Microsoft Excel) and .sav (IBM SPSS)</li> <li>Survey Codebook: includes information on all the variables and their coding (.xlsx)</li> <li>Data Management Plan.</li> </ul>
When is a Ring Road a 'Ring Road'? - results of the survey
<p>Images and results from the survey described in the paper "When is a Ring Road a 'Ring Road'? A Brief Perceptual Study".</p>
Survey questionnaire and results on Structural Barriers to Investment in Energy Efficiency Policies in the Private Rented Sector
<p>The online survey was designed and conducted in the framework of the EU H2020 project ENPOR ("Actions to Mitigate Energy Poverty in the Private Rented Sector). The aim of the survey was to receive statistically sound insights on structural factors that affect the implementation of energy efficiency policies for the alleviation of energy poverty in the European Private Rented Sector. We developed it as an explorative, semi-quantitative, self-completion online questionnaire, using the online tool “EUSurvey”. We performed the online survey among different stakeholders from academia, policy, NGO’s, landlords and tenant associations, etc.</p>
Survey questionnaire and results on Pay-for-Performance (P4P) schemes for energy efficiency measures
<p>The online survey was designed and conducted in the framework of the EU H2020 SENSEI project. The aim of the survey was to identify stakeholders’ perceptions on how P4P programmes could be integrated into the existing EU regulatory and market framework. We developed a semi-quantitative, self-completion online questionnaire, using the online tool “Alchemer”. The online survey collects input from different experts from the field of academia, consultancies, policymaking, and the energy industry. </p> <p>The questionnaire is used by <em>Tzani</em> <em>et al </em>(2022) to investigate how policy developments and adjustments in the EU can facilitate the design of performance-based energy efficiency programmes. The study combines a Strengths, Weaknesses, Opportunities, and Threats framework with an Analytical Hierarchy Process method and a Threats, Opportunities, Weaknesses, and Strengths matrix for the analysis of different stakeholder perceptions and the formulation of policy strategies.</p> <p>If you use this questionnaire in an academic publication, please cite the corresponding article:</p> <p><em>Tzani, D., Exintaveloni, D.S., Stavrakas, V., Flamos, A. </em><em>Devising policy strategies for the deployment of energy efficiency Pay-for-Performance programmes in the European Union</em><em>. </em></p>
Results from surveys to trainees participating into enriched sessions with practitioners
<p><strong>BACKGROUND:</strong></p> <p>The presented data was obtained during the implementation of the European project, DISCOVERY LEARNING. DISCOVERY LEARNING aims at experimenting towards a participatory, empowered and evolutionary work-based learning in PhD programmes for effectively training <strong>transferable skills</strong> related to Open Science and Innovation, achieving replicable and sustainable results over time. The ultimate goal was to establish a set of<strong> knowledge performance indicators (KPIs) about what works in doctoral education for training a set of transferable skills. </strong>If you want to get more information on the project, please click here:<strong> <a href="https://discoverylearning.eu/">https://discoverylearning.eu/</a> </strong></p> <p>At the level of <strong>pedagogical model</strong>, the chosen paradigm was the so-called <strong>Personalized Learning, </strong>which adjusts not only to the characteristics and needs of each student (adapting the pace and methodology), but also takes into account their particular preferences and interests (so that the student influences and actively participates in the definition of its objectives and in the selection of contents, as well as in the ways of demonstrating learning) (Looi et al., 2009).</p> <p>Within this double focus into transferable skills (related to open science and innovation) and the implementation of personalized learning mechanisms, the main experimental areas during the project were the following:</p> <ol> <li>Involving practitioners</li> <li>Making use of gamification</li> <li>Implementing real work-based learning</li> </ol> <p>On the basis of the above and as the result of a joint reflection process amongst partners, DISCOVERY LEARNING has formulated the following hypothesis:</p> <p><em>-If specific / punctual enriched activities by practitioners are inserted within the training programme, then:</em></p> <ul> <li><em>The level of engagement and performance improves significantly </em></li> <li><em> Institutions can train more students in transferable skills related to open science and innovation. </em></li> </ul> <p>In order to test and verify the above hypothesis, it has been decided to carry out a series of experimentation protocols (online and off-line trainings on a number of science & technology trends related to transferable skills) with early-stage researchers that can measure their applicability and reproducibility of our hypothesis over time. If you want to know more about the project results, please click here: <a href="https://discoverylearning.eu/results/traininglearning-options/dl-resources">https://discoverylearning.eu/results/traininglearning-options/dl-resources</a></p> <p><strong>METHODS:</strong></p> <p>We have designed an ad-hoc survey to gather relevant information from the participants of these experimentation protocols. These surveys will happen before and after the trainings.</p> <p>The following section show the surveys being used presently within the project to ask trainees around the aforementioned hypothesis. This survey has been refined in relation to the lessons learnt during stage 1 of the project’ experimentation strategy. For launching them, a professional software that allows optimising the design and User Experience, as well as managing the data gathered, was being used (<a href="https://app.surveyanyplace.com/">https://app.surveyanyplace.com/</a>)</p> <p> </p> <p><strong>• STATISTICAL INFORMATION GATHERED (general information):</strong></p> <p><strong>-Title: </strong>Surveys to trainees participating into enriched sessions with practitioners</p> <p><strong>-Method:</strong> Survey questions will incorporate Likert scale questions, multiple choice and free text answers.</p> <p><strong>-General information</strong>: Gender (optional), country, age range, discipline, internet connectivity and type and ownership of the device being used, language, institution.</p> <p><strong>-Information about the training in which the person is to participate / has participated:</strong></p> <p>Title and date. Unless desired different by the participant, the survey is anonymous.</p> <p><strong>-Period of surveying</strong>: 22.10.2021-19.03.2022</p> <p><strong>-Responsible person</strong>: Eva García Muntión (RTDI)</p> <p><strong><em>Terms of use:</em></strong> These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.</p> <p><strong><em>Funding:</em></strong><em> This project has received funding from the Research and Innovation framework Horizon 2020 of the European Union under grant agreement nº 101006452.</em></p> <p><br> </p>
Results of SPOT surveys for tourists, residents and entrepreneurs in the case studies - dataset
<p>This is a dataset of three surveys conducted within the scope of the SPOT project. The purpose of this dataset is to provide the results of the surveys for tourists, residents and entrepreneurs of the fifteen participating case studies. </p>
Results from Air Quality Monitoring and Surveys in UK Residences - Appendix (Survey Questions)
<p>Survey questions used in the paper titled "Results from Air Quality Monitoring and Surveys in UK Residences". Abstract below.</p> <p>Air pollution is a persistent issue in dwellings worldwide, costing an estimated 10-25 billion US dollars per year to the United Kingdom’s national health service alone. However, it is an “invisible problem” since background pollutants are often imperceptible except during acute pollution events such as wildfires. Although public awareness of ventilation has increased due to the COVID-19 pandemic, there are few tools available to assess its efficacy. Widely available sensor systems that can measure these pollutants tend to be single units with simple apps and little connection to mitigation, whereas different rooms in a house may have different pollution issues with different recommended actions. In this study, we present the results of a measurement study conducted using a multi-room sensor kit in twenty-nine dwellings across the UK. We also analyze the occupants’ reaction to the hardware, data, and a prototype alerting system. The study shows broad awareness of air quality in the participants. However, this awareness rarely corresponded to effective mitigation actions or ventilation provision. The concept of alerts was welcomed by participants if accompanied by actionable recommendations.</p> <p>The data showed significant pollution events, as measured by proxies such as total VOC and CO<sub>2</sub>, occurring almost daily, particularly in households with gas appliances. These incidents were concentrated around particular times of day and behaviors, indicating that the capacity of infiltration and extract ventilation to bring in adequate fresh air was overwhelmed. No significant outdoor pollution was detected in houses, which was expected given their sheltered peri-urban locations. The study highlights the need for comprehensive implementation of measurement, ventilation, and treatment measures in the UK housing stock to reduce the impact of indoor pollution on health.</p>
Supporting data sets for "Estimating Carbon Fixation of Plant Organs for Afforestation Monitoring using a Process-based Ecosystem Model and Ecophysiological Parameter Optimization". (the survey of tree breast diameter and tree height in 11-year old Eucommia ulmoides plantation, values of simulation results used in figures and tables.)
<p>Supporting data sets for Miyauchi et al., Ecology and Evolution, 2019 (accepted).</p> <p>The files store: </p> <p>(1) The survey of tree breast diameter and tree height in <em>Eucommia ulmoides</em> plantation<em>.</em> The ring and stem analysis and dry weight of seven harvested sample trees in the plantation.</p> <p>(2) Values of optimization result used fig.7.</p> <p>(3) Values of prediction result used fig.8. and table 4.</p> <p>(4) Values of optimized parameters by optimization methods, parameter range and constrain.</p>
Рис. 1. Αиния маршрута; цифры — места, гΑе быΛи отмечены особи бурого меΑвеΑя во время учетов с вертоΛета 22.05.2018. РезуΛьтаты учетов бурого меΑвеΑя на о. ЗавьяΛова с вертоΛета «Еврокоптер 120». 11:55 выΛет с нефтепирса г. МагаΑана, 12:14 поΑΛет к острову, 12:20 (1) отмечен первый моΛоΑой меΑвеΑь на террасе, 12:52 (2) отмечен оΑин взросΛый меΑвеΑь, 13:06 (3, 4) отмечены Αва взросΛых меΑвеΑя, 13:08 (5, 6, 7) отмечены три взросΛых меΑвеΑя, 13:18 (8) отмечен оΑин взросΛый меΑвеΑь. 13:56 переΛет в гороΑ МагаΑан Fig. 1. Route line; the figures indicate areas where brown bears were seen during the helicopter surveys on 22 May 2018. The results of the brown bear surveys on Zavyalov island from the Eurocopter 120 helicopter. 11:55 departure from the oil pier of Magadan, 12:14 hovering near the island, 12:20 (1) the first young bear identified on the terrace, 12:52 (2) one adult bear identified, 13:06 (3, 4) two adult bears identified, 13:08 (5, 6, 7) three adult bears identified, 13:18 (8) one adult bear identified, 13:56 Flight to Magadan in Brown bear (Ursus arctos) of Zavyalov Island (Sea of Okhotsk): Abundance and possible migration routes
Рис. 1. Αиния маршрута; цифры — места, гΑе быΛи отмечены особи бурого меΑвеΑя во время учетов с вертоΛета 22.05.2018. РезуΛьтаты учетов бурого меΑвеΑя на о. ЗавьяΛова с вертоΛета «Еврокоптер 120». 11:55 выΛет с нефтепирса г. МагаΑана, 12:14 поΑΛет к острову, 12:20 (1) отмечен первый моΛоΑой меΑвеΑь на террасе, 12:52 (2) отмечен оΑин взросΛый меΑвеΑь, 13:06 (3, 4) отмечены Αва взросΛых меΑвеΑя, 13:08 (5, 6, 7) отмечены три взросΛых меΑвеΑя, 13:18 (8) отмечен оΑин взросΛый меΑвеΑь. 13:56 переΛет в гороΑ МагаΑан Fig. 1. Route line; the figures indicate areas where brown bears were seen during the helicopter surveys on 22 May 2018. The results of the brown bear surveys on Zavyalov island from the Eurocopter 120 helicopter. 11:55 departure from the oil pier of Magadan, 12:14 hovering near the island, 12:20 (1) the first young bear identified on the terrace, 12:52 (2) one adult bear identified, 13:06 (3, 4) two adult bears identified, 13:08 (5, 6, 7) three adult bears identified, 13:18 (8) one adult bear identified, 13:56 Flight to Magadan
Results of survey to potential users of repositories of European Poetry on the informational needs
<p>This excel file presents the results of a survey to potential final users of digital repertoires of the European Poetry in order to understand their information needs. This file also presents results of the analysis to the survey: answers to the specific needs of the results - in black if the domain model responds to the needs; green if a new element to the domain is added due to the result.</p> <p>This survey was done in the context of the definition of a domain model for the european poetry. A domain model (or data model) is a milestone in the process of development of a metadata application profile.</p> <p>Together with this survey, other efforts were taken in place:</p> <p>1) Analysis of the structure of databases that serve digital repertoires of European Poetry</p> <p>2) Analysis of the information needs of graphical user interphaces of the digital repertoires of European Poetry</p> <p>The map https://goo.gl/O0mqhI presents the repertoires used and the phases of the analysis.</p> <p>This work is framed in a Starting Grant research project Poetry Standardization and Linked Open Data: POSTDATA (ERC-2015-STG-679528), funded by European Research Council (ERC).</p>
Results of a research software programming and development survey at the University of Reading
<p>In 2017 an online survey of University of Reading staff active in or supporting research and registered PhD students was undertaken to assess the nature and extent of research programming and software development activities in the University, and to understand how the University might provide guidance, training and support. The survey was a administered by the Research Data Manager on behalf of the University's Research Data Management Steering Group. The survey ran from 1st November to 15th December 2017 and collected a total of 170 responses.</p> <p>The survey sought responses from anyone in the University who was involved in any of the following activities:</p> <ul> <li>writing code and using software for numerical and statistical analysis;</li> <li>creating and contributing to computational models or simulations;</li> <li>conducting Text and Data Mining (TDM) and content analysis;</li> <li>creating and contributing to software distributed as a product or implemented as a service;</li> <li>creating data visualisations;</li> <li>using markup languages to structure and render content.</li> </ul> <p>The survey was distributed using the Bristol Online Survey. A dataset of anonymised survey responses and a PDF of the survey questions are here included.</p>
Survey Results - Use of new technologies for citizen participation
<p>This survey took place in Münster, Germany between April 07, 2016 and April 17, 2016. The goal of the survey was collect citizens' perspectives about new technologies' role for citizen participation. The survey questions, as well as its results are provided in German. The datasets features both the raw data (i.e., all answers preprocessed to keep all participants anonymous), and the summary of the survey results.</p>
University of Lausanne Institutional Open Access Survey Results
<p>This dataset contains the anonymized responses of 796 researchers from the institution who completed the 2017 institutional survey on Open Access.</p>
Task Scheduler Performance Survey Results
<p><strong>Task scheduler performance survey</strong></p> <p>This dataset contains results of task graph scheduler performance survey.<br> The results are stored in the following files, which correspond to simulations performed on <br> the `elementary`, `irw` and `pegasus` task graph datasets published at https://doi.org/10.5281/zenodo.2630384.</p> <ul> <li>elementary-result.zip</li> <li>irw-result.zip</li> <li>pegasus-result.zip</li> </ul> <p>The files contain compressed pandas dataframes in CSV format, it can be read with the following Python code:<br> ```python<br> import pandas as pd<br> frame = pd.read_csv("elementary-result.zip")<br> ```</p> <p>Each row in the frame corresponds to a single instance of a task graph that<br> was simulated with a specific configuration (network model, scheduler etc.).<br> The list below summarizes the meaning of the individual columns.</p> <ul> <li><strong>graph_name</strong> - name of the benchmarked task graph</li> <li><strong>graph_set</strong> - name of the task graph dataset from which the graph originates</li> <li><strong>graph_id</strong> - unique ID of the graph</li> <li><strong>cluster_name</strong> - type of cluster used in this instance the format is <number-of-workers>x<number-of-cores>; 32x16 means 32 workers, each with 16 cores</li> <li><strong>bandwidth</strong> - network bandwidth [MiB]</li> <li><strong>netmodel</strong> - network model (simple or maxmin)</li> <li><strong>scheduler_name</strong> - name of the scheduler</li> <li><strong>imode</strong> - information mode</li> <li><strong>min_sched_interval</strong> - minimal scheduling delay [s]</li> <li><strong>sched_time</strong> - duration of each scheduler invocation [s]</li> <li><strong>time</strong> - simulated makespan of the task graph execution [s]</li> <li><strong>execution_time </strong>- real duration of all scheduler invocations [s]</li> <li><strong>total_transfer</strong> - amount of data transferred amongst workers [MiB]</li> </ul> <p>The file `charts.zip` contains charts obtained by processing the datasets.<br> On the X axis there is always bandwidth in [MiB/s].<br> There are the following files:</p> <ul> <li>[DATASET]-schedulers-time - Absolute makespan produced by schedulers [seconds] </li> <li>[DATASET]-schedulers-score - The same as above but normalized with respect to the best schedule (shortest makespan) for the given configuration.</li> <li>[DATASET]-schedulers-transfer - Sums of transfers between all workers for a given configuration [MiB]</li> <li>[DATASET]-[CLUSTER]-netmodel-time - Comparison of netmodels, absolute times [seconds]</li> <li>[DATASET]-[CLUSTER]-netmodel-score - Comparison of netmodels, normalized to the average of model "simple"</li> <li>[DATASET]-[CLUSTER]-netmodel-transfer - Comparison of netmodels, sum of transfered data between all workers [MiB]</li> <li>[DATASET]-[CLUSTER]-schedtime-time - Comparison of MSD, absolute times [seconds]</li> <li>[DATASET]-[CLUSTER]-schedtime-score - Comparison of MSD, normalized to the average of "MSD=0.0" case</li> <li>[DATASET]-[CLUSTER]-imode-time - Comparison of Imodes, absolute times [seconds]</li> <li>[DATASET]-[CLUSTER]-imode-score - Comparison of Imodes, normalized to the average of "exact" imode</li> </ul> <p><strong>Reproducing the results</strong></p> <p><em>1. Download and install Estee (https://github.com/It4innovations/estee)</em></p> <p>$ git clone https://github.com/It4innovations/estee<br> $ cd estee<br> $ pip install .<br> <br> <em>2. Generate task graphs</em><br> You can either use the provided script `benchmarks/generate.py` to generate graphs<br> from three categories (elementary, irw and pegasus):</p> <p>$ cd benchmarks<br> $ python generate.py elementary.zip elementary<br> $ python generate.py irw.zip irw<br> $ python generate.py pegasus.zip pegasus<br> <br> or use our task graph dataset that is provided at https://doi.org/10.5281/zenodo.2630384.</p> <p><em>3. Run benchmarks</em><br> To run a benchmark suite, you should prepare a JSON file describing the benchmark.<br> The file that was used to run experiments from the paper is provided in<br> `benchmark.json`. Then you can run the benchmark using this command:<br> <br> $ python pbs.py compute benchmark.json</p> <p>The benchmark script can be interrupted at any time (for example using Ctrl+C).<br> When interrupted, it will store the computed results to the result file and restore<br> the computation when launched again.</p> <p><em>3. Visualizing results</em><br> <br> $ python view.py --all <result-file><br> <br> The resulting plots will appear in a folder called `outputs`.</p>
Socio-economic survey results
<p>Task 7.1 from WP7 required of the collection of socio-economic data from the implementation phase of the pilot measures in the 11 pilot cases in order to assess both impacts. An online survey was distributed among all stakeholders involved in the implementation of measures. With the exception of several open questions, most of the questions had a pre-defined range of answers. Questions were addressed to both management and staff levels. The dataset includes data relate to the following aspects:</p> <p><br> <strong>1. Social data</strong></p> <ul> <li>Gender</li> <li>Position in organization (management/staff)</li> <li>Work hours</li> <li>Workers</li> <li>Job satisfaction</li> <li>Training</li> <li>Acceptance</li> <li>Complaints from staff and tourists</li> <li>Awareness potential perception</li> <li>Willingness to adopt further actions</li> <li>Gender impact perception on various aspects</li> </ul> <p> </p> <p><strong>2. Economic data</strong></p> <ul> <li>Labor costs</li> <li>Equipment costs</li> <li>Material costs</li> <li>Economic savings</li> </ul> <p> </p> <p>The results of the data served as support for the evaluation of the socio-economic and gender impacts included in D7.1 of URBAN-WASTE.</p>
ScienceDex guides
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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.