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317 results for “Experts”
ELIXIR-CONVERGE - Survey of benefits of an Data Management expert network
<p>The aim of this short survey to was to gauge the perceived benefits of having established a network of Research Data Management professionals across the ELIXIR nodes, as part of the ELIXIR-CONVERGE project. </p> <p>Survey responses to the following questions were collected between 17 May and 11 July 2022 after an open invitation to the <a href="https://elixir-europe.org/about-us/how-funded/eu-projects/converge/wp1/dm-network">ELIXIR Data Management Network</a>:</p> <ul> <li>Is the ELIXIR Data Management Network providing you with any benefit? </li> <li>What benefits?</li> <li>Ideas for more ways of working?</li> <li>Are you associated with an ELIXIR node?</li> <li>What is your role?</li> </ul> <p>Included are survey responses raw data, and a pdf that summarises the responses.</p> <p> </p>
Interviews with experts on digitalisation in agriculture, forestry, and rural areas (H2020 DESIRA project, WP1)
<p>Interviews with experts on digitalisation in agriculture, forestry, and rural areas (H2020 DESIRA project, WP1).</p> <p>The scripts and the answers are provided. Two groups of experts have been interviewed: the first group with expertise in ICT, and the second group with expertise in socio-economic aspects. </p>
Sustainable Expert Criteria Weights
<p>This data was undertaken within the framework of the EU-funded <a href="https://www.leadproject.eu/">LEAD project</a> aiming to create Digital Twins for urban logistics networks in six cities to support experimentation in decision-making on-demand logistics operations in a public-private urban setting. The questionnaire consists of identifying priorities among different sustainability criteria related to last-mile logistics using a pair-wise comparison method to determine the experts' weights for every criterion.</p> <p>Gonzalez, J. N., Sobrino, N., & Vassallo, J. M. (2023). Considering the city context in weighting sustainability criteria for last-mile logistics solutions. <em>International Journal of Logistics Research and Applications</em>, 1–21. <a href="https://www.tandfonline.com/doi/full/10.1080/13675567.2023.2264788">https://doi.org/10.1080/13675567.2023.2264788</a></p>
BgMA-ESy: Expert system for automatic classification of vegetation plots of subalpine tall-herb vegetation (class Mulgedio-Aconitetea) from Bulgaria
<p>*****</p> <p>BgMA-ESy is an expert system that classifies vegetation plots of the class <em>Mulgedio-Aconitetea</em> (<a href="https://doi.org/10.1111/avsc.12257">Mucina et al. 2016</a>) occurring in Bulgaria. The expert system can be run using the JUICE program (<a href="https://doi.org/10.1111/j.1654-1103.2002.tb02069.x">Tichý 2002</a>; <a href="https://www.sci.muni.cz/botany/juice/">https://www.sci.muni.cz/botany/juice/</a>).</p> <p>The aggregation of vascular plants included within the BgMA-ESy is adopted from EUNIS-ESy (<a href="https://doi.org/10.1111/avsc.12519">Chytrý et al. 2020</a>; <a href="https://doi.org/10.5281/zenodo.4812736">https://doi.org/10.5281/zenodo.4812736</a>), and in a few cases, it is adjusted.</p> <p>*****</p> <p><strong>Specifications</strong></p> <p>The analyzed data (vegetation plots) cannot: </p> <ul> <li>include scrub vegetation (cover of tall shrub species > 8%; e.g., <em>Pinus mugo</em>, <em>Salix </em>spp.).</li> <li>contain tree species with cover > 1% (e.g., <em>Fagus sylvatica</em>, <em>Picea abies</em>).</li> <li>contain <em>Pteridium aquilinum </em>as a dominant species.</li> </ul> <p>The expert system was trained on vegetation plots with 5–100 m<sup>2</sup> area that occur above 1000 m a. s. l.</p> <p>* Exceptions from EUNIS-ESy aggregation:</p> <p>Heracleum sphondylium agg. does not include H. sphondylium subsp. verticillatum.</p> <p> </p> <p>*****</p> <p>When using this work, please cite:</p> <p>Szokala D., Kočí M. & Vassilev K. (2024): Subalpine tall-herb vegetation in Bulgaria: diversity and ecology. – Plant Biosystems 158: 490–510. <a href="https://doi.org/10.1080/11263504.2024.2327865">https://doi.org/10.1080/11263504.2024.2327865</a>.</p> <p>*****</p>
Results of the expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps
<p>This is the repository for the results of the 'expert opinion survey on environmental modeling with InVEST, Mapbiomas, and Open Street Maps'.</p> <p>Note: check the most recent version in the sidebar</p> <table> <tbody> <tr> <td>Current version</td> <td>v.0.2</td> </tr> <tr> <td>Date</td> <td>2024/01/10</td> </tr> <tr> <td>Respondants</td> <td>30</td> </tr> </tbody> </table> <p><strong>Available files:</strong></p> <table> <tbody> <tr> <td>File</td> <td>Type</td> <td>Description</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_public.csv">responses_v01_public.csv</a></td> <td>CSV table</td> <td>Survey raw results (anonymous)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_stats.csv">responses_v01_stats.csv</a></td> <td>CSV table</td> <td>Questions statistics</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_mean_sd.jpg">responses_v01_mean_sd.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (mean and standard deviation)</td> </tr> <tr> <td><a href="../api/files/a241155a-1fb9-4b1d-b2b4-3e5cca19ff4a/responses_v01_bands.jpg">responses_v01_bands.jpg</a></td> <td>JPEG Image</td> <td>Illustration of Stats (uncertainty bands)</td> </tr> </tbody> </table> <p>The column descriptions in the statistical table are as follows:</p> <p>Prefixes:</p> <ul> <li>HABITAT: habitat suitability score</li> <li>WEIGHT: Threat weight</li> <li>MAX_DIST: Maximum distance of negative influence (impact)</li> </ul> <p>Suffixes:</p> <ul> <li>mean: Average</li> <li>std: Standard deviation</li> <li>min: Minimum value</li> <li>p05: 5th percentile</li> <li>p25: 25th percentile</li> <li>p50: 50th percentile (median)</li> <li>p75: 75th percentile</li> <li>p95: 95th percentile</li> <li>max: Maximum value</li> </ul> <p>These prefixes and suffixes describe various statistical measures used to analyze the environmental modeling data.</p>
PRETEST AND POSTEST OF EXPERT SYSTEM FOR VOCATIONAL GUIDANCE
<p>Database on the process of vocational orientation in the I.E.P San Pedro - Quinocay in the province of Yauyos.</p>
CryoVirusDB: An Expert Labelled Cryo-EM Image Dataset for AI-Driven Virus Particle recognition and Extraction
<p><span>With the advancements in instrumentation, image processing algorithms, and computational capabilities, single-particle electron cryo-microscopy (cryo-EM) has achieved nearly atomic resolutions in the 3D reconstruction of viruses. These detailed structures play a crucial role in comprehending the biological functions and advancing the development of more precise vaccines and antiviral treatments. Despite the effectiveness of deep learning in analyzing microscopic images, its potential in identifying and extracting virus particles from cryo-EM micrographs has been hindered by the limited availability of diverse and high-quality datasets. In this study, we introduce 'CryoVirusDB,' a labeled dataset containing coordinates of accurately selected virus particles in cryo-EM micrographs. CryoVirusDB comprises 9,941 micrographs featuring 9 different viruses along with the coordinates of 0.2 million virus particles in total. We anticipate that CryoVirusDB will enhance the capabilities of deep learning in accurately identifying virus particles in cryo-EM micrographs, thereby facilitating the subsequent 2D-3D reconstruction process.</span></p> <p><span>Instructions to download and use dataset: https://github.com/BioinfoMachineLearning/CryoVirusDB</span></p>
PhasAGE Ask The Expert Series | Salvador Ventura
<p>A collection of captivating interviews with senior researchers from the PhasAGE consortium. Meet each core member while they foster new scientific horizons and cultivate collaborations, through clear, engaging dialogue that connects science with society. This initiative was organized by the early-stage researchers together with the Research and Communication Manager.</p> <p><span>This interview is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952334 – PhasAGE.</span></p>
PhasAGE Ask The Expert Series | Peter Tompa
<p>A collection of captivating interviews with senior researchers from the PhasAGE consortium. Meet each core member while they foster new scientific horizons and cultivate collaborations, through clear, engaging dialogue that connects science with society. This initiative was organized by the early-stage researchers together with the Research and Communication Manager.</p> <p>This interview is part of a project that has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 952334 – PhasAGE. </p>
Canada's national artificial intelligence governance system: Dataset from interviews with 20 government leaders & subject matter experts
<p><strong>Summary</strong></p> <p>Anonymized aggregate data from interviews with 20 government leaders and subject matter experts. The data was collected as part of a study of Canada's national system of artificial intelligence governance. The data was collected from February 2023 to July 2023. The dataset contains 610 topics that emerged from thematic analysis of interview transcripts from July 2023 to October 2023. The contexts, actors, resources, networks, evaluations, logics, functional bounds, rules, ecosystem-level dynamics, opportunities for improvement, and other topics contained in the dataset collectively represent the most significant components of Canada's national AI governance system that emerged over the course of the interviews with the 20 participants.</p> <p> </p> <p><strong>Notes for interpreting this dataset</strong></p> <p>Topics in analytical dimensions 1, 3, and 6-11 contain counts of the frequency with which aggregate topics emerged across each of the interviews with the 20 participants. Topics in analytical dimensions 2, 4, and 5 contain categories instead of frequency counts: the topics in these dimensions represent every unique actor, resource, and network that emerged over the course of the interviews instead of aggregate topics. </p> <p>Column titles contain the following abbreviations:<br>LEAD: Interviews with leaders of public sector AI governance initiatives.<br>SME-PS: Interviews with subject matter experts employed in the private sector.<br>SME-CS: Interviews with subject matter experts employed in the academic or civil sectors.</p> <p> </p> <p><strong>Full report</strong></p> <p>A report containing more information about this dataset and about the findings of our study can be found on SSRN: <a href="https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4783525">https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4783525</a></p> <p> </p>
Dataset_Experts_Evaluation_for_Integrated_MCDM_Approach_for_PGT_Selection_in_SES
<p>Template and Dataset containing experts' evaluation for the case study of the research paper entitled "Evaluation_for_Integrated_MCDM_Approach_for_PGT_Selection_in_SES"</p>
Low carbon energy R&D portfolios that are robust when models and experts disagree
<p>This data archive contains model runs and data analysis files to the research article</p> <p><strong>Low carbon energy R&D portfolios that are robust when models and experts disagree</strong></p> <p>by</p> <p>Franklyn Kanyako, Erin Baker, David Anthoff</p> <p> </p> <p><strong>All Model output and Non-Dominated Portfolios</strong>: This contains all expected values of all model outputs, used to determine the non-dominated portfolios under each policy.</p> <p><strong>Large Scale Expert Elicitation of R&D Investment</strong>: Contains samples of expert elicitation from each elicitation team.</p> <p> </p> <p> </p>
Expert Finding Benchmark Datasets (IR, CL and SW communities)
<p>This is the updated version of the original benchmark expert finding datasets proposed by the authors of this paper - <a href="https://doi.org/10.1145/2508497.2508501">https://doi.org/10.1145/2508497.2508501</a>. The current version is released as part of Neural Expert Finder (NEF), a novel expert finding approach utilizing transformer based pre-trained language models.</p>
Dataset of the paper "Machine learning for expert-level image-based identification of very similar species in the hyperdiverse plant bug family Miridae (Hemiptera: Heteroptera)"
<p>This dataset contains 3792 images of 26 plant bug (Insecta: Heteroptera: Miridae: Mirini) species used to test the performance of a CNN in species recognition. All jpg files are 1920 pixels on the long size and additionally available as an archive file to facilitate download of the entire dataset. </p> <p>Bar code labels (unique specimen identifiers or USIs) were attached to all examined specimens used for this study. Further information such as additional photographs of habitus and genitalic structures, georeferenced coordinates of each locality, specimens dissected, notes, collecting method can be obtained from the Heteroptera Species Pages (http://research.amnh.org/pbi/heteropteraspeciespage/) which assembles available data from a specimen database and are also provided as an Excel spreadsheet (file _Adelphocoris_CNN_label_data.xlsx).</p>
Wikipedia video games similarity dataset with expert annotations
<p>A video games NLP dataset extracted from Wikipedia.</p> <p>For all articles, the figures and tables have been filtered out, as well as the categories and "see also" sections.</p> <p>The article structure, and particularly the sub-titles and paragraphs are kept in these picese.</p> <p>Provided as well are 90 seeds with recommended articles, annotated by human experts.</p>
Interview data on experts' recommendations for visualizations in libraries
<p>Results of an interview study with twelve experts on their project processes and their recommendations for visualizations in libraries. Recommendations were retrieved with the method SHIRA (Structured Hierarchical Interviewing for Requirement Analysis)[1]. This allows generating concrete qualities and implementation suggestions out of abstract qualities.</p> <p>The file contains two pages: On the first, a meta-model was constructed out of all identified steps during library visualization projects. On the second, all SHIRA suggestions were gathered and analyzed.</p> <p>Feel free to contact me if you have any questions!</p> <p> </p> <p> [1] M. Hassenzahl, R. Wessler, and K.-C. Hamborg. Exploring and understanding product qualities that users desire. Conference on Human-Computer Interaction IHM-HCI’2001, 2, 2001.</p>
Dataset for learning to predict the suitability of experts for statements
<p>Dataset consisting of 1,700 (statement, expert) pairs for learning and evaluating whether a researcher can be cited as an expert for a statement.<br> A total of 170 statements were extracted from the dataset "De Argumentenfabriek" [1].<br> For each statement, two experts were manually selected from Google-Scholar and their data extracted.<br> In addition, for each of the 170 statements, 8 experts were randomly picked from the pool of 340 scientists and then manually assessed on a scale from 0 to 2 by two to three annotators whether that scientist can be cited as an expert for the statement.</p> <p>[1]: https://zenodo.org/record/4813727<br> </p>
JOSSE: A Software Development Effort Dataset Annotated with Expert Estimates
<p>The JIRA Open-Source Software Effort (JOSSE) dataset consists of software development and maintenance tasks collected from the JIRA issue tracking system for Apache, JBoss, And Spring open-source projects. All the issues were annotated with actual effort and 19% of them were annotated with expert estimates. JOSSE is a task-based dataset with a textual attribute represented as a task description for each data point. This paper explains how the data were collected and details six data quality refinement procedures of the data points.</p>
EXPLORE Expert Data Challenges 2022 - Craters dataset
<p>This dataset contains Lunar Craters images and labels in COCO json format that will be used for the EXPLORE Expert Data Challenge 2022.</p> <p> </p> <p>More information at: https://exploredatachallenges.space/</p> <p> </p> <p>Source dataset is derived from Fairweather et al. (2022</p> <p>Images were processed from NASA PDS raw data and labels extracted using python scripts. </p>
EXPLORE Expert Data Challenges 2022 - Boulders dataset
<p>This dataset contains Lunar Craters images and labels in COCO json format that will be used for the EXPLORE Expert Data Challenge 2022.</p> <p> </p> <p>More information at: https://exploredatachallenges.space/</p> <p> </p> <p>Source dataset is derived from Watkins, Ryan (2019)</p> <p>Images were processed from NASA PDS raw data and labels extracted using python scripts. </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.