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Dataset results
7 results for “Science Achievement”
Demo showing what RELIANCE project has achieved on Open Science, FAIR and EOSC
<p>This demo shows what we have achieved on Open Science and FAIR. </p> <p> </p> <p>- Starting from <a href="https://beta.explore.openaire.eu/">OpenAIRE EXPLORE</a>, we search for "Copernicus air quality" and find lots of resources, mostly publications and only 2 software. The reason is that to be "classified" as "Software", we have to add specific metadata when publishing.</p> <p>- The "Software" we found is a "EOSC Jupyter notebook" created by <a href="https://orcid.org/0000-0003-3979-3645">Simone Mantovani</a> with a DOI and additional metadata so that OpenAIRE explore can "associate" it to a specific EOSC service, namely <a href="https://www.egi.eu/services/notebooks/">EGI Notebook</a>. </p> <p>- When we click on "<a href="https://marketplace.eosc-portal.eu/services/egi-notebooks?q=EGI+Notebook">EOSC Service: EGI Notebook</a>", we are re-directed directly to the service that has been used to generate the original scientific results we found in OpenAIRE explore.</p> <p>- Any EOSC service needs to be requested and you have to plave an "order" to get access to it, where you may have to explain why you would like to access this EOSC service. To authenticate to any EOSC service, you can use for instance your <a href="https://orcid.org/">ORCID </a>identifier. if you do not have one, we suggest to register: this is very handy for EOSC services and you keep your ORCID identifier when you move from one institution to another (in addition, your institutional login may not work).</p> <p>- You will get notified by email (check your SPAM folder!) when you got access to an EOSC service.</p> <p>- We login to EGI notebook using ORCID identifier and upload (manually) the jupyter notebook we found in OpenAIRE (following the link e.g. from zenodo (<a href="https://doi.org/10.5281/zenodo.5554786">https://doi.org/10.5281/zenodo.5554786</a>)</p> <p>- The Jupyter notebook uses CAMS European air quality analysis from Copernicus Atmosphere Monitoring Service. The input data is accessible through an external service called the <a href="https://reliance.adamplatform.eu/">ADAM platform</a> (Advanced geospatial Data Management platform). It hosts datacubes (easy and fast access to large amount of data).</p> <p>- We can re-execute the Jupyter notebook but more importatnly we can create derivative work. However, make sure you check the license of the original result you find in OpenAIRE explore: it needs to have a license that allows you to create derivative work. Also make sure the Jupyter notebook is well documented.</p> <p>- We duplicate the Jupyter notebook and customize it. To bring the Open Science aspect from the beginning and not only when publishing the Jupyter Notebook, we need to use storage that can be shared. We use another service called "<a href="https://www.egi.eu/services/datahub/">EGI datahub</a>".</p> <p>- As when collaboratively writing scientific papers, we agree on how to organize the data: we create an "input folder" (containing all the input datasets used in the Jupyter notebook), an "output" folder with all the outputs we generate and a tool folder with the Jupyter notebook.</p> <p>- The new analysis is very similar to the previous one but over a different geographical area (France). </p> <p>- Finally, we create a Research Object that aggrgate all the resources. We use another external service called <a href="https://reliance.rohub.org/">RoHub </a> (Research Object Hub) and create and "executable Research Object" which we hope will be found, accessed and reused!</p> <p> </p>
Dataset: Achieve Life Sciences, Inc. (ACHV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Achieve Life Sciences, Inc. (ACHV) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size
<ol> <li>Population size is a key metric for management and policy decisions, yet wildlife monitoring programs are often limited by the spatial and temporal scope of surveys. In these cases, citizen science data may provide complementary information at higher resolution and greater extent.</li> <li>We present a case study demonstrating how data from the eBird citizen science program can be combined with regional monitoring efforts by the U.S. Fish and Wildlife Service to produce high-resolution estimates of golden eagle abundance. We developed a model that uses aerial survey data from the western United States to calibrate high-resolution annual estimates of relative abundance from eBird. Using this model, we compared regional population size estimates based on the calibrated eBird information to those based on aerial survey data alone.</li> <li>Population size estimates based on the calibrated eBird information had strong correspondence to estimates from aerial survey data in two out of four regions, and population trajectories based on the two approaches showed high correlations.</li> <li>We demonstrate how the combination of citizen science data and targeted surveys can be used to (a) increase the spatial resolution of population size estimates, (b) extend the spatial extent of inference, and (c) predict population size beyond the temporal period of surveys. Findings based on this case study can be used to refine policy metrics used by the U.S. Fish and Wildlife Service and inform permitting regulations (e.g., mortality/harm associated with wind energy development).</li> <li> <em>Policy implications</em>. Our results demonstrate the ability of citizen science data to complement targeted monitoring programs and improve the efficacy of decision frameworks that require information on population size or trajectory. After validating citizen science data against survey-based benchmarks, agencies can harness strengths of citizen science data to supplement information needs and increase the resolution and extent of population size predictions.</li> </ol>
Leveraging the strengths of citizen science and structured surveys to achieve scalable inference on population size
Open the record for dataset details and reuse information.
Data from: Sectorial pathways to achieve net-zero and 1.5°C targets for Eu-27: Energy and emissions data to inform science-based decarbonization targets
Open the record for dataset details and reuse information.
Engagement and social impact in tech-based Citizen Science initiatives for achieving the SDGs : A Systematic Literature Review with a perspective on complex thinking
<p>Data set</p>
ScienceDex guides
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International Brain Laboratory public data
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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.