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619 results for “Adoption”
ALL-READY Questionnare on potential drivers and barriers to the adoption of innovation management, open science, and Intellectual Property Rights (IPR) among the members of the Pilot Network
<p><strong>Background & Summary</strong>: </p><p>The ALL-READY project unites a diverse consortium of Research Infrastructures (RI) and Living Labs, instrumental in developing new methodologies and technologies in agroecology. The project focuses on effective management of innovation, adherence to open science principles, and strategic application of Intellectual Property Rights (IPR). Task 6.4 of the project, which concentrates on Innovation and IPR Management, seeks to understand the dynamics influencing the adoption of these practices among its members. Recognizing the need for end-to-end data management, the project emphasizes standardized data collection and management while adhering to FAIR principles.</p><p><strong>Methods</strong>: </p><p>The questionnaire was developed by LifeWatch ERIC to capture data reflecting current practices and perceptions in agroecology. It included 26 questions divided into four sections, focusing on existing practices, potential drivers, and barriers in innovation management, open science, and IPR. The survey was disseminated via an online platform to the ALLREADY Pilot Network, ensuring a representative sample from diverse organizations. The data collection process was closely monitored, and the responses were analyzed using a mixed-methods approach to extract meaningful insights.</p><p><strong>Data Records of the ALLREADY Project Questionnaire</strong>: </p><p>The dataset, collected through an online survey platform, underwent a meticulous process of data preparation, download, formatting, and anonymization. It consists of one text file containing metadata (Readme.txt) and a single CSV file encompassing all questionnaire responses. The dataset provides a comprehensive view of innovation management, open science adoption, and IPR handling within the agroecology sector, particularly among the network of RIs and Living Labs involved in the project.</p><p><strong>Technical Validation of the ALLREADY Project Questionnaire</strong>: </p><p>Several critical steps were taken to ensure the accuracy, reliability, and overall quality of the data collected. This included development and testing of the questionnaire, rigorous monitoring of the data collection process, and thorough checks for data quality and completeness. The representativeness of the sample was analyzed specifically with respect to the Pilot Network rather than the broader population involved in agroecology. Strategies were employed to counter survey fatigue and maintain respondent engagement.</p><p><strong>Usage Notes for the ALLREADY Project Questionnaire</strong>: </p><p>The dataset's proper usage is vital for ensuring the validity and reproducibility of research. Researchers are advised to consider the nature of the data, the representativeness of the dataset, and its generalizability. The dataset allows for comprehensive analysis and integration of different sections, and analysts have the flexibility to handle open and write-in responses according to their research needs. Additional information to facilitate analysis is provided in a separate documentation file.</p><p> </p>
Co-design – Part 2: Workshop with professionals, early-adopters, and late/non-adopters to design interventions for a more responsible and just future with smart home technologies
<h3>Description</h3> <p>This qualitative dataset is the <strong>second part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a series of two <strong>in-person workshops</strong>: one with professionals developing smart technology and its early-adopters, and a second one with late/non-adopters of smart technology. The first workshop had four groups of participants and the second three groups. The data is divided by each group. The data collected during the previous and subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from workshop with professionals and early-adopters</h3> <ul> <li><strong>P2_WSP-PA-G1-TRANSCR_R00.docx</strong> (transcription of group 1 audio recordings) <ul> <li><strong>P2_WSP-PA-G1-VIS_000 </strong>to <strong>_005</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G2-TRANSCR_R00.docx </strong>(transcription of group 2 audio recordings) <ul> <li><strong>P2_WSP-PA-G2-VIS_000 </strong>to<strong> _008</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G3-TRANSCR_R00.docx </strong>(transcription of group 3 audio recordings) <ul> <li><strong>P2_WSP-PA-G3-VIS_000 </strong>to<strong> _004</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-PA-G4-TRANSCR_R00.docx </strong>(transcription of group 4 audio recordings) <ul> <li><strong>P2_WSP-PA-G4-VIS_000 </strong>to<strong> _002</strong> (participant-generated visual data)</li> </ul> </li> </ul> <p> </p> <h3>Documents from workshop with late/non-adopters</h3> <ul> <li><strong>P2_WSP-LN-G1-TRANSCR_R00</strong> (transcription of group 1 audio recordings) <ul> <li><strong>P2_WSP-LN-G1-VIS_000 </strong>and<strong> _001</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-LN-G2-TRANSCR_R00</strong> (transcription of group 2 audio recordings) <ul> <li><strong>P2_WSP-LN-G2-VIS_000 </strong>to<strong> _003</strong> (participant-generated visual data)</li> </ul> </li> </ul> <ul> <li><strong>P2_WSP-LN-G3-TRANSCR_R00</strong> (transcription of group 3 audio recordings) <ul> <li><strong>P2_WSP-LN-G3-VIS_000 </strong>to<strong> _002</strong> (participant-generated visual data)</li> </ul> </li> </ul> <p> </p> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
History, Adoption and Key impacts of precision agriculture
<p>Precision agriculture technologies have revolutionized modern farming practices, offering innovative solutions to optimize crop production, minimize resource use, and enhance environmental sustainability. This research paper explores the historical evolution, adoption trends, and importance of precision agriculture technologies in contemporary agriculture.</p>
Database of permacultural adoption responses in Mexicali, BC, Mexico. based on Circular Economy, Knowledge Management, and Sustainability policies
<p>Database documenting the perspectives of citizens in Mexicali, Baja California, Mexico, regarding the adoption of permaculture practices. The study is analyzed through the lenses of Knowledge Management, Circular Economy, and Sustainability Policies. The data was collected during the summer of 2024. </p>
Dataset for the publication "The TACS Model: Understanding Teachers' Adoption of Computer Science Pedagogical Content in Primary School"
<p>This dataset contains the quantitative teacher data used to analyse an in service teacher training program for Computer Science that took place from September 2019 to March 2020 in the Canton Vaud in Switzerland. Approximately 180 teachers from the the 5th and 6th grade in primary school (ages 9-11) participated in 3 days of training sessions. At the end of each training session, teachers were asked to fill in a web-based questionnaire providing information relating to their perception of the training sessions and adoption of the computer science activities. The surveys were analysed from three perspectives which are detailed in the corresponding article (the professional development program's perspective, the activities' perspective, the teacher's perspective). The present repository thus contains three csv files, one per analysis. A README is included and provides additional information regarding :</p> <p>- the requirements for re-use. </p> <p>- the survey instrument used</p> <p>- the specific content of the 3 csv files</p>
ORCID data to accompany Study of ORCID Adoption Across Disciplines and Locations
<p>Data gathered in January 2017 from the ORCID registry in support of Study of ORCID Adoption Across Disciplines and Locations. Study conducted as part of Horizon 2020 project THOR (http://project-thor.eu).</p> <p><strong>ORCID uptake by discipline and region.xlsx</strong> : Master file of all processed metrics with breakdowns by discipline and region. (For description of selection of disciplinary taxonomy and processing steps, see associated paper.)</p> <p><strong>All other .csv files</strong> : Underlying data for processed metrics. (For description of "full counts" and other factors of data gathering, see associated paper.)</p>
Accompanying material to the Inventory of opportunities and bottlenecks in policy to facilitate the adoption of soil-improving techniques
<p>Inventory of policies at EU and country level for the inventory and analysis of bottlenecks and opportunities in sectoral and environmental policies to facilitate the adoption of Soil-Improving Cropping Systems (SICS).</p>
[Dataset] FP-Redemption: Measuring Browser Fingerprinting Adoption for the Sake of Web Security
<p>Full dataset for the paper "FP-Redemption: Measuring Browser Fingerprinting Adoption for the Sake of Web Security"</p> <p>5 files are provided:</p> <ul> <li>dataset.csv. The raw elements collected when browsing the web. Each entry corresponds to one attribute being accessed with one parameter combination by one script on one webpage. A single attribute with the same parameters can be accessed several times. It is represented with the key <em>nbTimes</em></li> <li>domainTags.csv: For each website, it provides its category and country tag.</li> <li>webpageTags.csv: For each webpage, it provides its type.</li> <li>fingerprinters.zip/<filenumber>.js: Our fingerprinters. Out of the 199 we requested, 7 are missing, leading in 192 js files.</li> <li>mapping.csv. 3 columns CSV file: <ul> <li>The first one lists the 199 fingerprinters detected by our algorithm.</li> <li>The second one gives the <filenumber> used to link a fingerprinter and its file in the directory.</li> <li>The third one gives the groups the fingerprinters belongs to. By default, each fingerprinter belongs to his own group. However, several fingerprinters are belonging to the same group as we evaluate there were duplicates. Thus, the number of distinct groups corresponds to the distinct fingerprinters we measured in our dataset: 169.</li> </ul> </li> </ul>
Co-design - Part 4: Evaluative interviews with professionals, early-adopters, and late/non-adopters about the co-design process
<h3>Description</h3> <p>This qualitative dataset is the <strong>fourth part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a <strong>online semi-structured interviews </strong>with professionals developing smart technology, its early-adopters, and late/non-adopters. The interviews were conducted at Microsoft Teams. The data collected during the previous parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from focus group</h3> <ul> <li><strong>P4_INTV-P</strong><em>X</em><strong>-TRANSCR.docx</strong><strong> </strong>(transcriptions of online interviews with professionals)</li> <li><strong>P4_INTV-A</strong><em>X</em><strong>-TRANSCR.docx </strong>(transcriptions of online interviews with early-adopters)</li> <li><strong>P4_INTV-N</strong><em>X</em><strong>-TRANSCR.docx </strong>(transcriptions of online interviews with late/non-adopters)</li> </ul> <p> </p> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Co-design – Part 3: Focus group with professionals, early-adopters, and late/non-adopters to better detail each intervention designed, who would be involved, and how it would happen
<h3>Description</h3> <p>This qualitative dataset is the <strong>third part</strong> of a PhD study on co-designing smart home technologies, and represents the data collected during a <strong>online focus group</strong> with professionals developing smart technology, its early-adopters, and late/non-adopters. The meeting was conducted at Microsoft Teams with the support of an visual board on Miro. The data collected during the previous and subsequent parts of the referred study are also available at Zenodo.</p> <h3> </h3> <h3>Documents from focus group</h3> <ul> <li><strong>P3_FG-PAN-TRANSCR_R00.docx </strong>(transcription of focus group audio recordings)</li> </ul> <p> </p> <h3>Acknowledgements</h3> <p>This study is part of the GECKO Project (<a href="https://gecko-project.eu/">https://gecko-project.eu/</a>) and has received funding from the European Commission under the Horizon2020 MSCA-ITN-2020 Innovative Training Networks programme, Grant Agreement No 955422 (<a href="https://cordis.europa.eu/project/id/955422">https://cordis.europa.eu/project/id/955422</a>).</p>
Figure 3: Scheme of the procedure adopted for implementing the Sand Dune Acts of 1903/1908, to reclaim the lands affected by sand drifting
<p>Figure 3 of article: Managing Coastal Sand Drift in the Anthropocene: A Case Study of the Manawatū-Whanganui Dune Field, New Zealand, 1800s–2020s</p> <p>DOI zenodo: 10.5281/zenodo.5075980</p>
A Survey on Adoption Guidelines for the FAIR4RS Principles: Dataset
<p>A list of 30+ online resources have been identified and curated by the FAIR4RS Subgroup 5: Adoption Guidelines. These resources are available as the supplementary materials of the report (<a href="https://doi.org/10.5281/zenodo.6374598">Martinez et al., 2022</a>) and can be downloaded and cited from this landing page.</p> <p><strong>The list is open for additions by the community via comments directly to this <a href="https://docs.google.com/spreadsheets/d/1pMWEyadkGW22zYaBl3LsNYoQVk9AW711-2Ogyi8UZZo/edit#gid=0">link</a>. We particularly encourgae authors of new and exisiting resources to add as much detail as possible to describe their resource and its relevance to the <a href="http://doi.org/10.15497/RDA00068">FAIR4RS Principles</a>. Each of the columns has a description and whether the information is optional or not. Whe plan to add tags to the added resources for each semester and when there is another set of 30 resources we can resealease a new version. </strong></p> <p>This list reflects the wide spectrum of global contributions supporting the implementation of the FAIR Principles, particularly regarding research software. It is a snapshot of currently available resources, although we expect that new resources will become available in the future and that the contents of the current list will evolve. It is important to note that most of these resources precede the definition of the <a href="https://doi.org/10.15497/RDA00068">FAIR4RS Principles</a>; however, these still support their implementation.</p> <p>The resources were manually collected, analyzed, and categorized according to their type: guidelines, tools, metadata schemas and registries/repositories. For each resource detail is also provided on which of the FAIR4RS Principles that the resource supports.</p> <p> </p> <p><strong>Data collection</strong></p> <p>This subgroup initiated a crowdsourcing effort to identify relevant resources. All members had the opportunity to provide and describe existing FAIR research software guidelines and tools. During the first two months of the subgroup operation in 2021, subgroup participants (referred to as data providers) added resources to an online spreadsheet. Data providers were encouraged to list resources that they were aware of, authored, or were supported by their institutions. Subsequently, the subgroup organized virtual calls to discuss the resources, their descriptions and the categorization. Over the next two months each data provider added descriptions to resources they were familiar with. This meant that some resources gained descriptions from different data providers. Before the completion of the list, the subgroup leads checked the list and cleaned it (removing items that lacked information or providing complementary information). The resulting list is the first crowdsourced list of its type and it welcomes your contributions!</p>
Database_Appliance_Adoption_Patterns
<p>Preliminary version of the database of appliance adoption patterns in recently electrified settlements in developing countries.</p>
Raw (main) dataset for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Raw dataset for farmer responses to a survey on the decision support systems adoption for intergrated pest management in the framework of the EU funded project IPM Decisions.</p>
Vine Growers adoption of IPM CVP PDO
<p> Data from an online survey was conducted with 275 winegrowers from the Conegliano Valdobbiadene Prosecco Protected Designation of Origin area. Data were analysed using Structural Equation Modelling and a Multinomial Probit Model, revealing four out of five dimensions as components of winegrowers’ EA. The results demonstrated that increased EA significantly and positively affects the likelihood of adopting organic and Integrated Pest Management protocols.</p>
Appendix. Generic nomenclature adopted in the present paper for the nominal species of the Tortonian, which were previously reported under open generic nomenclature. in Tortonian teleost otoliths from northern Italy: taxonomic synthesis and stratigraphic significance
Appendix. Generic nomenclature adopted in the present paper for the nominal species of the Tortonian, which were previously reported under open generic nomenclature.
Figure 2 in Does nutritional status constrain adoption of more costly and less risky foraging behaviour in an Amazonian shelter-building spider?
Figure 2. Relationship between predicted probability of spider Hingstepeira folisecens (Hingston 1932) (Araneidae) exhibiting a pulling foraging behaviour to catch prey and body condition index (BCI – standardized residuals from a regression of abdomen volume on carapace area) in one region of Central Amazonia, Brazil. '1' represents occurrence of pulling behaviour and '0' represents absence of spider response or use of pursuing behaviour (n = 19).
Hackathons as a Pedagogical Strategy to Engage Students to Learn and to Adopt Software Engineering Practices
<p>Teaching Software Engineering is not a trivial duty since several pedagogical strategies can be used and sometimes the impact of these on students is uncertain. Hackathons are similar to marathons, however used to produce solutions to solve a specific problem in a short period of time and based on intense collaboration. Educational hackathons aim to promote learning in such an environment. The Undergraduate computing programs of PUCRS decided to use a hackathon as a pedagogical strategy aiming to motivate the students to practice the adoption of software development practices and to work in groups as a means to practice the development of social skills. Therefore, we conducted a case study to investigate: 1) The motivations to students to attend or not attend an educational hackathon, 2) The students perceptions about this hackathon, 3) The Software Engineering practices adopted by students. In this study, we identified factors that may affect students motivation to participate (e.g., improve the teamwork skills), some students expectations about the hackathon (e.g., work in teams), and the practices adopted by the students (e.g., pair programming). Some of our findings include that students enjoy participating in an informal educational environment (e.g., hackathons) to improve their technical skills and to build network with some colleagues. This study can provide insights to teachers that wants to organize some activity than traditional teaching and the students perspective about this kind of strategy.</p>
A Survey on the Adoption of Patterns for Engineering Software for the Cloud - Response Dataset
<p>This work takes as a starting point a collection of patterns for engineering software for the cloud and tries to find how they are regarded and adopted by professionals. We investigate (1) their relevance for professional software developers, (2) the extent to which product and company characteristics influence their adoption, and (3) how adopting some patterns might correlate with the likelihood of adopting others. For this purpose, we surveyed 102 practitioners using an online questionnaire. </p> <p>Amongst other findings, we conclude that most companies are using these patterns, with the overwhelming majority (97%) using at least one. We observe that the mean pattern adoption tends to increase as companies mature, namely when varying the product operation complexity, active monthly users, and company size. Finally, we establish clear correlations in the adoption of specific pairs of patterns, with conditional probabilities as high as 94%, which hints on how some practices are dependent or influence the adoption of others.</p>
Figure 9. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 9. - Information flows between EU BON and LTER Europe, as envisaged on the 3rd EU BON Stakeholder Roundtable in Granada on 9-11 December 2015.
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.