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619 results for “Adoption”
Figure 8. 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 8. - The patchiness of survey coverage in Europe illustrated by the distribution map of Plantago lanceolata taken from GBIF in 2016. This species is one of the commonest and most widespread in Europe, it should occur in almost all areas of this map, but in fact the data traces out the borders of countries and area who have published data on GBIF.
Figure 6. 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 6. - Individual Metacat instances can be connected to DataOne which replicates public files. Thus the data is still available if a single instance goes offline.https://search.dataone.org/#data/page/0
Figure 5. 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 5. - PPBio has installed a Metacat instance for their researchers to upload and make publicly available the results of work related to biodiversity in the Western Amazon.https://ppbiodata.inpa.gov.br/metacatui/
Figure 4. 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 4. - The public data repository provided by the Knowledge Network for Biocomplexity (KNB).https://knb.ecoinformatics.org/#data/page/0
Figure 3. 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 3. - The implementation of Darwin Core Archive in Plazi to transfer treatment data. Observation data described with Darwin Core terms.
Figure 1. 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 1. - ARPHA consists of two integrated workflows: in ARPHA-XML, the manuscript is written and processed via the ARPHA Writing Tool, and in ARPHA-DOC, the manuscript is submitted and processed as document file(s).
Data for: Drivers and Barriers for Microservice Adoption in the German Software Industry
<p>Microservices are an architectural style for software which currently receives a lot of attention in both industry and academia. Several companies employ microservice architectures with great success, and there is a wealth of blog posts praising their advantages. Especially so-called Internet-scale systems use them to satisfy their enormous scalability requirements and to rapidly deliver new features to their users.<br> However, microservices are not only popular with large, Internet-scale systems. Many traditional companies are also considering whether microservices are a viable option for their applications. However, these companies may have other motivations to employ microservices, and see other barriers which may prevent them from adopting microservices. Furthermore, these drivers and barriers may differ among industry sectors.<br> This dataset contains the questions and results of a survey on drivers and barriers for microservice adoption among professionals in the German software industry. In addition to overall drivers and barriers, we particularly focused on the use of microservices to modernize existing software, with special emphasis on implications for runtime performance and transactionality.</p>
Explaining non-adoption of electronic government services by citizens. A study among non-users of public e-services in Latvia
<p>This data was collected as part of the H2020 Citadel project, http://www.citadel-h2020.eu, project no. 726755. The objective was to analyse citizen motives for not using electronic government services. Using interviews among users of Citizens´ Service Centres in Latvia, the data is used to analyses the motives of citizens not to use electronic government services but to rely on non-electronic equivalents or on in-person assistance. Findings and fieldwork details are available in D2.1 of this project.</p>
Hashtag adoption time series
<p>The dataset released here has been used in our paper "#Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags." [link to the paper](https://www.aaai.org/ocs/index.php/ICWSM/ICWSM13/paper/view/6083/6376)</p> <p>In this study, we examine the growth, survival, and context of over 250 novel hashtags during the 2012 U.S. presidential debates. Our analysis reveals the trajectories of hashtag use fall into two distinct classes: "winners" that emerge more quickly and are sustained for longer periods of time than other "also-rans" hashtags. Statistical analyses of the growth and persistence of hashtags reveal novel relationships between the hashtags' contextual features and the relative success of hashtags. This is the first study on the lifecycle of hashtag adoption and use in response to purely exogenous shocks, which has implications for understanding social influence and collective action in social media more generally.</p> <p>The dataset was the hashtag adoption time sequences during the four debates. They are used to create **Figure 1** in the paper (Cumulative tweet volume of hashtags over time, starting from each debate). </p> <p>Each csv file has three columns:<br> time (in UTC), y (the minute-by-minute cumulative tweet count), and tag (the hashtag name).</p> <p>The onset of the four debates are:<br> Debate 1: 2012-10-04 00:00:00 UTC<br> Debate 2: 2012-10-12 00:00:00 UTC<br> Debate 3: 2012-10-17 00:00:00 UTC<br> Debate 4: 2012-10-23 00:00:00 UTC</p> <p>The raw tweet data have been released via the ICWSM Data Sharing Service. See: <br> [http://www.icwsm.org/2013/datasets/datasets/](http://www.icwsm.org/2013/datasets/datasets/)</p> <p> </p> <p><strong>Publication</strong><br> If you make use of these data sets and code, please cite:</p> <p>Lin, Y.-R., Margolin, D., Keegan, B., Baronchelli, A., Lazer, D. (2013). #Bigbirds Never Die: Understanding Social Dynamics of Emergent Hashtags. In Proceedings of the 7th International AAAI Conference on Weblogs and Social Media (ICWSM 2013) </p>
Historical Adoption of Technologies (HATCH) Dataset
<h4>About the Dataset </h4><p>The Historical Adoption of TeCHnologies (HATCH) dataset provides yearly data of technology adoption levels at both the global and country levels. The dataset includes a heterogeneous set of technologies that differ in their size, use, and spatial diffusion. The following files are available.</p><h4>Citation</h4><p>The paper can be cited as: Nemet, G., Greene, J., Müller-Hansen, F. <i>et al.</i> Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures. <i>Commun Earth Environ</i> <strong>4</strong>, 397 (2023). https://doi.org/10.1038/s43247-023-01056-1 </p><p><a href="https://www.nature.com/articles/s43247-023-01056-1"><strong>Paper describing the data is available here: </strong>https://www.nature.com/articles/s43247-023-01056-1</a></p><p> </p><h4>File Descriptions</h4><p><strong>HATCH1.0.csv</strong></p><p><i>Description: </i>Dataset used in the analysis for "Dataset on the adoption of historical technologies informs the scale-up of emerging carbon dioxide removal measures," which includes a limited number of country-level datapoints.</p><p> </p><p><strong>HATCH_v1.5_Clean.csv</strong></p><p> </p><p><i>Description: </i>Expanded dataset that includes country-level adoption of many technologies used in v1.0, with the addition of more global technology time series.</p><p> </p><p><strong>Tech_Growth_V1.5_variabledescriptions_Clean.yaml</strong></p><p><i>Description: </i>Description of each technology, including description, metric, and technology category.</p><p> </p><p><strong>HATCH_v1.5_DataSources.csv</strong></p><p><i>Description: </i>Lists the data sources and corresponding citations for each technology in the HATCH dataset.</p><p> </p><p>Code for calculating growth rates available at:<a href="https://zenodo.org/records/8327347 "> DOI: <strong>10.5281/zenodo.8327347</strong></a></p><p> </p><p>Table of growth rate calculations for each technology available at:<a href="https://zenodo.org/records/8327347"> </a><a href="https://zenodo.org/records/10056128">https://zenodo.org/records/10056128</a></p><p> </p><p><strong>Link to Scenario Explorer hosted by IIASA: </strong><a href="https://cdr.apps.ece.iiasa.ac.at/story/hatch/">https://cdr.apps.ece.iiasa.ac.at/story/hatch/</a></p><p> </p>
Global present-day air-conditioning adoption rate
<p>This dataset contains the present-day, global, survey-based, and spatially explicit air-conditioning adoption rate dataset developed in Li et al. (2024), “Enhancing Urban Climate-Energy Modeling in the Community Earth System Model (CESM) through Explicit Representation of Urban Air-conditioning Adoption”, published in <em>Journal of Advances in Modeling Earth Systems</em>. It also contains the simulation results analyzed in the article. Details about this dataset (data sources, data collection and processing methods, simulation setup, etc.) are described in the article. The air-conditioning adoption rate dataset is publicly available in tabular, vector, and gridded formats. It is compatible with CESM, and can also be leveraged in other climate and energy modeling applications and socioeconomic or integrated assessment analyses. This dataset may be useful for multiple scientific communities regarding urban climate and energy, impacts, vulnerability, risks, and adaptation applications. </p> <p>For more detailed description, please refer to the README file (<em>global_AC_adoption_rate_README.txt</em>) included in the dataset.</p>
Data and code from: Unoccupied aerial systems adoption in agricultural research
<div> </div> <p>This repository contains data and code supporting the findings of the study on the adoption of Unoccupied Aerial Systems (UAS) in agricultural research as reported by Lachowiec et al (2024) in The Plant Phenome Journal.</p> <p>We collected data through an online survey as well as through in person interviews.</p> <div> <h2>Description of Repository Contents</h2> </div> <div> <h3>Data</h3> </div> <p>Data are in the <code>/data</code> directory:</p> <ul> <li><code>Ag_Drones_Codebook_14Jun2023.pdf</code>: Codebook providing detailed descriptions of survey questions and coding schemes. This contains detailed descriptions of the content of the two CSV files listed below.</li> <li><code>Results_Ag_Drones_2021_Survey.csv</code>: This is raw survey data collected from agricultural researchers regarding their use of UAS technology.</li> <li><code>countries_code.csv</code>: Country codes used in the survey data for respondent location.</li> <li><code>interviews/</code>: A directory containing interview transcripts and summary provided as both Microsoft Word and plain text (Markdown) formats, specifically: <ul> <li>Notes from nine one on one interviews named <code><interviewee last name>)UAS_Interview.[md|docx]</code></li> <li>A summary document, <code>Feldman_AG2PI_InterviewSummary_2022-08-10.docx</code>.</li> </ul> </li> </ul> <div> <h3>Code</h3> </div> <p>Code used to process data and generate the manuscript's analysis and figures.</p> <ul> <li><code>data_code.R</code>: R Script for preprocessing and cleaning the survey data.</li> <li><code>dataAnalysis.R</code>: R script for statistical analysis and visualization of survey results.</li> </ul> <div> <h2>Citing this work</h2> </div> <p>This repository contains data and code to support the manuscript:</p> <blockquote> <p>Lachowiec, J., Feldman, M.J., Matias, F.I., LeBauer, D., Gregory, A. (2024). Unoccupied aerial systems adoption in agricultural research. Zenodo. The Plant Phenome Journal Volume(Issue), pages 00. doi:DOI</p> </blockquote> <p>If you use the data or code from this repository, please also cite:</p> <blockquote> <p>Lachowiec, J., Feldman, M.J., Matias, F.I., LeBauer, D., Gregory, A. (2024). Data and code from: Unoccupied aerial systems adoption in agricultural research. Zenodo. doi:10.5281/zenodo.10573428</p> </blockquote> <p>And consider contributing cleaned data and code to this repository.</p> <div> <h2>Acknowlegements and Support</h2> </div> <p><strong>Acknowledgments</strong></p> <p>We thank all survey respondents for their participation. We acknowledge the Montana State University HELPS lab for aiding in the development and implementation of the survey.</p> <p><strong>Funding</strong></p> <p>This research was supported by the intramural research program of the U.S. Department of Agriculture, National Institute of Food and Agriculture, Agricultural Genome to Phenome Initiative (2020-70412-32615 and 2021-70412-35233). The findings and conclusions in this preliminary presentation have not been formally disseminated by the U. S. Department of Agriculture and should not be construed to represent any agency determination or policy.</p>
Curated dataset for analysis for the paper "Decision Support Systems Adoption in Pesticide Management"
<p>Dataset created from 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>
DASP: A Framework for Driving the Adoption of Software Security Practices
<p>Online appendix for the publication:</p> <p>DASP: A Framework for Driving the Adoption of Software Security Practices.</p>
i-SoMPE Inventory A: Adoption rate of 58 innovative soil management practices
<p>Adoption rate of 58 innovative soil management practices (maps of inventory A)</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>
Recording of ETAPAS 4th Dissemination Event: Tools for the ethical and trustworthy adoption of Artificial Intelligence in the service of public administrations
<p>On the 12th of October 2022 the 4th ETAPAS dissemination event "Tools for the ethical and trustworthy adoption of Artificial Intelligence in the service of public administrations" took place online and on site at the Centre for Research & Technology Hellas (CERTH).</p> <p>If you missed the workshop you can find here the recording of the event, where we presented the first outputs generated by the ETAPAS Project to Greek Public Administration in order display concrete results on how AI can be ethically embedded in their activities.</p> <p>Among the topics we discussed:</p> <ul> <li>the ETAPAS Project and key tools developed for the good governance of AI;</li> <li>the chatbot Kari and the challenges using AI-based solutions presents;</li> <li>the development of a misinformation detection platform by CERTH;</li> <li><a href="https://www.pop-ai.eu/">popAI</a> and <a href="https://token-project.eu/the-project/">TOKEN</a> projects;</li> <li>strategies for a governance framework for artificial intelligence in public administration.</li> </ul> <p>Check this recording to see all the interesting presentations and discussions that emerged during the project.</p>
Supplementary material 1 from: Yemshanov D, Koch F, Ducey M, Haack R, Siltanen M, Wilson K (2013) Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker's perspective. NeoBiota 18: 193-218. https://doi.org/10.3897/neobiota.18.4002
Risk of out-of-state (out-of-province) locations to be the source of forest pests transported in firewood carried by campers. The risk rank values are based on the delineation of nested non-dominant sets via the first-degree stochastic dominance rule (FSD). The ranks close to 1.0 denote the highest risk of pest arrival and the ranks close to 0 denote the lowest risk. (doi: 10.3897/neobiota.18.4002.app1) File format: Adobe PDF File (pdf).:
Supplementary material 4 from: Yemshanov D, Koch F, Ducey M, Haack R, Siltanen M, Wilson K (2013) Quantifying uncertainty in pest risk maps and assessments: adopting a risk-averse decision maker's perspective. NeoBiota 18: 193-218. https://doi.org/10.3897/neobiota.18.4002
Summary of differences between risk rank classes, 0–0.05, 0.05–0.25, 0.25–0.5, 0.5–0.75, 0.75–0.95 and 0.95–1 in the delineations based on the FSD and SSD rules. (doi: 10.3897/neobiota.18.4002.app4) File format: Adobe PDF File (pdf).:
Рис. 11. Гидрометеоролого-технологическая блок-схема хоЗяйственных решений (di) и гидрометеорологических долгосрочных прогноЗов (Pi), необходимых для их принятия. Fig. 11. Hydrometeorological-technological block diagram of economic decisions (di) and hydrometeorological long-term forecasts (Pi) necessary for their adoption. in Review of methods for the forecast of mollusk's spat productivity in sea-farms of Primorye and probable ways of their enhancement
Рис. 11. Гидрометеоролого-технологическая блок-схема хоЗяйственных решений (di) и гидрометеорологических долгосрочных прогноЗов (Pi), необходимых для их принятия. Fig. 11. Hydrometeorological-technological block diagram of economic decisions (di) and hydrometeorological long-term forecasts (Pi) necessary for their adoption.
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.