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47,155 results for “evaluations”
A challenging data set for evaluating part-of-speech taggers
<p>This data set contains 2,227 sentences, with a part-of-speech (POS) tag specified for a single word in the sentence. The data file is a tab-separated text file where each row (after the header row) is formatted as follows:</p><p><i>sentence <TAB> POS tag <TAB> (optional) motivation</i></p><p>Note that, in the sentence (= a string of space-separated characters), the POS-tagged word is indicated by the POS tag in brackets, placed just after the word to which it refers. Example:</p><p><i>The road bends [VERB] to the right . VERB</i></p><p>In this example, the optional motivation is not included, as the tagged word can easily be identified as being a verb.</p>
Datasets of synthetic task graphs for evaluating a reliability and latency multi-objective task allocation framework
<p>These datasets of synthetic task graphs were generated to evaluate the performance and scalability of a multi-objective task allocation approach for workflow applications of various structures and sizes in a system based on the edge-hub-cloud paradigm. The targeted architecture comprised an edge device (e.g., a single-board computer attached to an unmanned aerial vehicle (UAV)) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. The objectives were the maximization of the overall reliability and the minimization of the overall latency of the application, under memory, storage, energy, and task precedence constraints. We considered that a percentage of the tasks required fixed allocation on the edge or hub device. Each task had a different vulnerability factor (i.e., probability of failure) on each device.</p> <p>We generated nine task graphs of serial, parallel, and mixed (a combination of serial and parallel) structure with 10, 100, and 1000 nodes, utilizing the Task Graphs For Free (TGFF) random task graph generator [1]. Additional task parameters (e.g., execution time, power consumption, vulnerability factor, memory, storage, output data size) were included post-generation, using representative random values. More details are provided in README.txt.</p> <p>Note: These datasets are released under a Creative Commons Attribution license. If you utilize these datasets in your work, please cite us using the corresponding Zenodo DOI https://doi.org/10.5281/zenodo.10357101.</p> <p>References:</p> <p>[1] R. P. Dick, D. L. Rhodes and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE'98), Seattle, WA, USA, 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p>
Forschungsdaten zur Evaluation der Sichtbarkeit von Interdisziplinarität in institutionellen Repositorien
<p>Die Forschungsdaten gehören zu der abgeschlossenen Masterarbeit "Eine Evaluation zur Sichtbarkeit von Interdisziplinarität in institutionellen Open-Access-Repositorien", welche auf dem edoc-Server der HU Berlin veröffentlicht wird. Der Abstract zur Arbeit lautet:</p> <p>Interdisziplinäre Forschung ist ein seit vielen Jahrzehnten besprochenes und praktiziertes Konzept, um verschiedene Facetten eines Themas einander ergänzend untersuchen und reale Probleme ohne disziplinäre Einengungen lösen zu können. Institutionelle Repositorien sind etablierte digitale Infrastrukturen von Institutionen wie Universitäten, relevant bei der Open-Access-Transformation und dienen neben der Archivierung und Veröffentlichung von wissenschaftlichen Publikationen auch der Erzeugung von nachnutzbaren beschreibenden Metadaten. Bibliotheken beschäftigen sich mit der Abbildung von Publikationen und betreiben überwiegend auch die genannten Repositorien. Einen direkten Weg zur Recherche von interdisziplinären Veröffentlichungen scheint es allerdings nicht zu geben, genauso fehlt eine wissenschaftliche Auseinandersetzung eben jener Sichtbarkeit in bibliothekarischen Systemen wie z. B. Repositorien. Zur Erforschung dieser scheinbaren Differenz gegenüber dem Stellenwert in der wissenschaftlichen Praxis unterteilt sich die Arbeit in drei Teilstudien, um mit unterschiedlichen Methoden sich ergänzende Erkenntnisse zu ermöglichen. Neben einer qualitativen Analyse von händisch ausgewählten Metadatensätzen interdisziplinärer Publikationen, wurde auch eine quantitative Analyse eines größeren Datensatzes auf Grundlage der wörtlichen Nennung sowie eine überregionale Untersuchung von Repositorien direkt in den Suchportalen durchgeführt. In den Ergebnissen ließ sich feststellen, dass es Wege zu interdisziplinären Publikationen gibt, die aber nicht aktiv mit diesem Ziel konzipiert sind oder gestaltet werden. Das Zusammenspiel von unter anderem an Organisationsstrukturen orientierten Sammlungen, disziplinärer Klassifikation, Selbsteingabe durch die Autor:innen sowie nicht-hierarchischen Metadatenstrukturen macht es in jedem Fall nötig, die Publikationen einzeln zu prüfen. Die Identifizierung in der Itemansicht wird zwar in der Regel nicht strukturell unterstützt, ist aber auf Basis der üblichen Eingaben und Informationen aus dem Volltext möglich.</p> <p><span> </span></p>
FAIR Evaluations of University and College Repositories at DataCite Using MetaDIG Mappings for Four Use Cases.
<p>This spreadsheet has the results of an evaluation of FAIRness of 387 University and College DataCite repositories using techniques developed in the MetaDIG project. It is possible to compare scores from different repositories and to create rose diagrams showing the results for any of the repositories.</p>
Jacdac: Service-based Prototyping of Embedded Systems (PLDI 2024 Artifact Evaluation)
<p>This artifact allows others to reproduce and explore the results seen in "Jacdac: Service-based Prototyping of Embedded Systems". The artifact contains a prebuilt docker image and the Dockerfile source used to produce the prebuilt docker image. Evaluators should follow the README contained in this artifact for complete instruction.</p>
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>
Datasets of synthetic workflows for evaluating a multi-objective and multi-constrained scheduling approach for cyber-physical applications
<p>These datasets of synthetic workflows (task graphs) were generated to evaluate the performance and scalability of a multi-objective and multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) based on the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors with different processing core failure rates and varied sensing/actuating or other specialized capabilities. Our objectives were the minimization of the overall latency, the minimization of the overall energy consumption, and the maximization of the overall reliability of the workflow application in the specific CPS, under deadline, reliability, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random task graphs with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability, reliability threshold) were included post-generation, using appropriate values. More details are provided in README.txt.<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," Proceedings of the Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.<br>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p>
CORDEX AFR44 Model Evaluation Data for the Melka-Wakena Watershed, Ethiopia
<ul> <li> <p>#Overview<br>This dataset supports the study on Africa-CORDEX Regional Climate Models' performance evaluation in simulating air temperature (tasmax and tasmin) and precipitation in the Melka-Wakena watershed, Ethiopia. <br>The evaluation spans from 1991 to 2005 and includes observed daily data and raw CORDEX AFR44 daily data for tasmax, tasmin, and precipitation. The dataset was analyzed using scatter plots, empirical cumulative distribution functions (ECDF), Taylor diagrams, and multi-metric performance evaluations.</p> <p>#Files and Structure<br>1. Data/<br>>>This directory contains all input and processed datasets used in the study.</p> <p>#Observed_Data/</p> <p>>>Observed_Tasmax.csv: Observed daily maximum temperature (tasmax) data.<br>>>Observed_Tasmin.csv: Observed daily minimum temperature (tasmin) data.<br>>>Observed_Precipitation.csv: Observed daily precipitation data.</p> <p>#CORDEX_Raw_Data/</p> <p>>>CORDEX_Tasmax_Raw.csv: Daily tasmax data from CORDEX AFR44 models.<br>>>CORDEX_Tasmin_Raw.csv: Daily tasmin data from CORDEX AFR44 models.<br>>>CORDEX_Precipitation_Raw.csv: Daily precipitation data from CORDEX AFR44 models.</p> <p>#Processed_CORDEX_Data/</p> <p>>>CORDEX_Tasmax_Processed.csv: Preprocessed daily tasmax data for analysis (e.g., aggregated and formatted).<br>>>CORDEX_Tasmin_Processed.csv: Preprocessed daily tasmin data for analysis.<br>>>CORDEX_Precipitation_Processed.csv: Preprocessed daily precipitation data for analysis.</p> <p>2. Scripts/<br>This directory contains Python scripts used for preprocessing, evaluation, and visualization of the data.</p> <p>#Data_Preprocessing_Scripts/</p> <p>>>Preprocess_Tasmax.py: Script to preprocess daily tasmax data.<br>>>Preprocess_Tasmin.py: Script to preprocess daily tasmin data.<br>>>Preprocess_Precipitation.py: Script to preprocess daily precipitation data.</p> <p>#Evaluation_Scripts/</p> <p>>>Scatter_Plot_Script.py: Script for scatter plot visualizations comparing observed and model data.<br>>>ECDF_Script.py: Script for generating empirical cumulative distribution functions (ECDF).<br>>>Taylor_Diagram_Script.py: Script for generating Taylor diagrams to evaluate model performance.<br>>>Performance_Metrics_Script.py: Script to compute evaluation metrics.<br>>>Approach_Comparison_Script.py: Script for comparing different model evaluation approaches using multi-metric weighted ranking.</p> <p>#Metadata<br>>>Study Area: Melka-Wakena watershed, Ethiopia.<br>>>Time Period: 1991–2005.<br>#Data Source:<br>>>Observed data from local meteorological stations.<br>>>CORDEX AFR44 model data downloaded from the Earth System Grid Federation (ESGF).</p> <p>#Variables:<br>Tasmax: Daily maximum temperature (°C).<br>Tasmin: Daily minimum temperature (°C).<br>Precipitation: Daily precipitation (mm/day).<br>Evaluation Metrics: RMSE, MAE, R², NSE, Percent Bias (PBIAS) ,and others.</p> <p>#How to Use<br>Download the dataset:<br>All required data files are organized in the Data/ folder.</p> <p>#Run the preprocessing scripts:<br>>>If using new datasets, preprocess the raw data using the scripts in Data_Preprocessing_Scripts/. This step formats the data and ensures compatibility with the evaluation scripts.</p> <p>#Conduct evaluation:</p> <p>>>Use the Evaluation_Scripts/ to replicate scatter plots, ECDF, Taylor diagrams, and compute performance metrics.<br>>>Use Approach_Comparison_Script.py for multi-metric weighted ranking comparisons of model performance.</p> <p>#Citation<br>>>When using this dataset, please cite the following:</p> <p>#The dataset:<br>"Dataset for CORDEX AFR44 Model Evaluation in the Melka-Wakena Watershed, Ethiopia."<br>DOI: https://doi.org/10.5281/zenodo.14208274</p> <p>#The source of CORDEX data:<br>>>CORDEX AFR44 data, available from the Earth System Grid Federation (ESGF).</p> <p>Contact<br>For questions or additional information, contact:</p> <p>Tadele: t4shgeresu@gmail.com.</p> </li> </ul>
Wind tunnel test data for the evaluation of the aerodynamic coefficients of an antenna mast with ancillaries.
<p>This dataset comprises measured data and results from static wind tunnel tests conducted in April 2024 at the Giovanni Solari Wind Tunnel Facility (GS-WinDyn). The tests aim to assess the drag, lift, and moment coefficients <span>of an antenna mast designed as a triangular lattice tower, equipped with both linear and discrete ancillary components.</span> The wind tunnel experiments are carried out under both smooth and turbulent flow conditions using a scaled 3D model of the antenna mast. Five ancillary configurations, based on predominant patterns observed, are tested. Drag forces, lift forces and moments are measured using two six-component force balances attached to the ends of the model, while downstream three-component velocity data is captured by a Cobra probe. For each configuration, aerodynamic coefficients are determined for angles of attack ranging from 0° to 360°, with increments of up to 10°. The dataset provides the measured data and the obtained aerodynamic coefficients and it has significant reuse potential in several applications: comparison with experimental wind tunnel data, validation of analytical and numerical CFD models with similar configurations, estimation of wind loads due to ancillary structures, and characterization of wake effects.</p>
Evaluation datasets and results of the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing"
<p>Event logs, process models, and results corresponding to the paper "Efficient Online Computation of Business Process State From Trace Prefixes via N-Gram Indexing".</p> <p><em><strong>Inputs</strong></em>: preprocessed event logs and discovered process models (and their characteristics) used in the evaluation.</p> <ul> <li><em><strong>Real-life</strong></em>: preprocessed event logs (<em>xes</em> and <em>csv</em>) corresponding to the real-life processes used in the evaluation. Process models (<em>pnml</em>) discovered with the Inductive Miner infrequent for thresholds of 10%, 20%, and 50%. Characteristics (<em>txt</em>) of the event logs and process models. Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>).</li> <li><em><strong>Synthetic</strong></em>: simulated event logs (<em>csv</em>) corresponding to the synthetic processes used in the evaluation. Designed process models (<em>bpmn</em> and <em>pnml</em>). Ongoing cases result from splitting each case in the preprocessed event logs (under folder <em>split</em>). Ongoing cases with injected noise as described in the publication (under folders <em>noise_1</em>, <em>noise_2</em>, and <em>noise_3</em>).</li> </ul>
Compiled database, code and raw data for the article "A Comprehensive Database of Leaf Temperature, Water, and CO2 Fluxes in Young Oil Palm Plants Across Diverse Climate Scenarios for the Evaluation of Functional-Structural Models"
<p>This dataset results from an experiment on young oil palm plants (<em>Elaeis guineensis</em>) in the Ecotron facility from CNRS in Montpellier. Four plants were put in a microcosm one by one with varying climatic conditions to investigate the effect of climate on leaf temperature, CO2, and H2O fluxes at the plant scale. The conditions were defined based on typical daily conditions from a location where it is grown (Libo, Indonesia), <em>i.e.</em>, a day with no rainfall and near-average air temperature and humidity. This base condition was then modified by adding more CO2 (400, 600 and 800ppm), less radiation (typical cloudy sky), and more or less temperature and vapour pressure deficit (± 30%).</p> <p>Find more details from the <code>README.md</code> file in the repository or from the associated <a href="https://github.com/PalmStudio/Biophysics_database_palm" target="_blank" rel="noopener">Github repository</a>.</p>
Evaluating Nitrate Removal and Travel Times in a Bare Deciduous Forest Soil Using a Tracer-Based Soil Column Experiment
<p> </p> <p>This dataset summarizes the experimental data from a study investigating the relationship between hydrodynamic properties, transport age characteristics (traced using the conservative tracer bromide), and nitrate consumption by native microbial communities in a deciduous forest soil near Lausanne, Switzerland.</p> <h4><strong>Experimental Setup</strong></h4> <ul> <li>Native soil was <strong>collected, sieved, and packed</strong> into homogeneous soil columns (lysimeters) with a porosity of <strong>0.4</strong>.</li> <li>To stimulate microbial activity, the soil underwent <strong>carbon amendment</strong> for several weeks before the experiment.</li> <li>On <strong>June 10, 2021</strong>, a <strong>pulse of bromide and nitrate</strong> (referred to as <strong>SPIKE</strong>) was applied to the bare soil that had undergone carbon amendment.</li> <li>The dataset covers experimental conditions recorded from <strong>May 25, 2021, to August 12, 2021</strong>.</li> </ul> <h4><strong>Included Data & Models</strong></h4> <ol> <li><strong><strong>SPIKE experiment (<strong>pulse of bromide and nitrate</strong>)</strong></strong> <ul> <li> <ul> <li><strong><strong>all other files (see table_of_contents.html and overview.pdf)</strong></strong></li> </ul> </li> </ul> </li> <li> <p><strong>HYDRUS-1D Simulations</strong></p> <ul> <li>The dataset includes HYDRUS-1D simulation files to reproduce <strong>nitrate and bromide breakthrough curves</strong>: <ul> <li><strong>0_Nitrate-double-compartment.zip</strong></li> <li><strong>0_Bromide-double-compartment.zip</strong></li> </ul> </li> </ul> </li> <li> <p><strong>Soil Characterization using Multi-Step Outflow (MSOM) Method</strong></p> </li> </ol> <ul> <li> <ul> <li>Before the main experiment, the <strong>multi-step outflow method</strong> was performed to characterize the soil. The original experimental data was collected at a <strong>15-minute resolution</strong> but has been <strong>upscaled to hourly resolution</strong> to reduce noise and facilitate data handling.<br> <ul> <li><strong>multi-step_outflow_method.csv</strong></li> </ul> </li> </ul> </li> </ul> <ol> <li> <ul> <li>The processed data (at <strong>hourly resolution</strong>) and <strong>HYDRUS-1D inverse modeling simulations</strong> for soil parameter estimation are provided under two configurations: <ul> <li><strong>config_1_MSOM.zip</strong></li> <li><strong>config_2_MSOM.zip</strong></li> </ul> </li> </ul> </li> </ol> <p> </p>
Systematic Evaluation and Usability Analysis of Formal Tools for Railway System Design - Technical Annexes
<p>This package includes additional data for the paper ``Systematic Evaluation and Usability Analysis of Formal Methods Tools for Railway Signalig System Design'', by Alessio Ferrari, Franco Mazzanti, Davide Basile, and Maurice ter Beek, CNR-ISTI, Italy, accepted for publication in the IEEE Transactions on Software Engineering, DOI: 10.1109/TSE.2021.3124677</p> <p>The paper concerns the systematic evaluation and usability analysis of 14 formal tools for system design, namely CADP (2020-g), FDR4(4.2.7), NuSMV(1.1.1), ProB(1.9.3), Atelier B (4.5.1), Simulink (R2020a), SPIN (6.4.9), UMC (4.8), UPPAAL (4.1.4), mCLR2 (202006.0), SAL (3.3), TLA+ (2) and CPN Tools (4.0). The current package includes the following content:</p> <ol> <li>Tool Evaluation Template and .pdf: a document including the reference evaluation template, and the evaluation sheet of each tool. </li> <li>Tool Evaluation Table.xlsx: a table summarizing the results of the evaluation.</li> <li>System Usability Test - SUS Results.xlsx: an excel file with multiple sheets with all the raw results of the usability test for the tools.</li> </ol>
Embedding Evaluation Data for South African Languages
<p><strong>WordSim and Simlex Data for South African Languages</strong></p> <ul> <li>Setswana</li> <li>Sepedi</li> </ul> <p><strong>Embedding Evaluation Data for South African Languages</strong></p> <p><strong>Dataset Information\</strong></p> <p>The datasets(Simlex and WordSim) contain pairs of Setswana and Sepedi words that have been assigned similarity ratings by humans to measure semantic relatedness. The word-pairs(Simlex and WordSim) are manually translated from English to Setswana and Sepedi. The evaluation task aims to find the degree of correlation between the scores provided by the model and the human rating, the score of the model is collected by computing the cosine similarity of corresponding vectors for word pairs.</p> <p>Online Repository link</p> <ul> <li><a href="https://zenodo.org/record/5673974">Zenodo Data Repository</a> - Link to the data repository.</li> </ul> <p>Authors</p> <ul> <li><strong>Vukosi Marivate</strong> - <a href="https://twitter.com/vukosi">@vukosi</a></li> <li><strong>Valencia Wagner</strong></li> <li><strong>Mack Makgatho</strong></li> <li><strong>Tshephisho Sefara</strong></li> </ul> <p>See also the list of <a href="https://github.com/dsfsi/embedding-eval-data//contributors">contributors</a> who participated in this project.</p> <p>Citing the dataset</p> <p>To appear in conference proceedings</p> <blockquote> <p>@article{Makgatho_Marivate_Sefara_Wagner_2022, title={Training Cross-Lingual embeddings for Setswana and Sepedi}, <br> volume={3}, <br> url={https://upjournals.up.ac.za/index.php/dhasa/article/view/3822}, <br> DOI={10.55492/dhasa.v3i03.3822}, <br> number={03},<br> journal={Journal of the Digital Humanities Association of Southern Africa },<br> author={Makgatho, Mack and Marivate, Vukosi and Sefara, Tshephisho and Wagner, Valencia}, <br> year={2022}, <br> month={Feb.}}</p> </blockquote>
Image dataset for the evaluation of a low-cost high-throughput plant phenotyping system
<p>This dataset contains the raw and processed images from a low-cost high-throughput plant phenotyping (HTP) system, as well as the raw and processed images that were manually acquired for comparison. The HTP images were automatically and wirelessly acquired for entire benches of plants with a system composed of a Raspberry Pi and eight GoPro cameras. The entire file system of each GoPro camera was copied directly into a subfolder of finalGoProImages (numbered by camera). The raw HTP images were processed by correcting for lens distortion, computing the "greenness index" for each individual pixel, and filtering out extreme high and low values. These processed HTP images were then saved in the "greenness" subfolder of finalGoProImages. The manually acquired images in the finalDSLR folder each represent an individual plant from one of five time points during the same greenhouse experiment. The raw manually acquired images were processed in the same manner as the raw HTP images by computing the greenness index for each individual pixel and filtering out extreme high and low values. The two tab-delimited text files include the number of green pixels and mean greenness index for each HTP (greennessGoProTable2.txt) and manually acquired (greennessDSLRTable2.txt) image.</p>
epiGBS2: Improvements and evaluation of highly multiplexed, epiGBS-based reduced representation bisulfite sequencing
<p>We present epiGBS2, a laboratory protocol based on epiGBS (Gurp et al., 2016) with a revised and user-friendly bioinformatics pipeline for a wide range of species with or without reference genome. Performance of several critical steps in epiGBS2 was evaluated against baseline data sets from <em>Arabidopsis thaliana</em> and Great tit (<em>Parus major</em>), which confirmed overall good performance of epiGBS2. We provide here the raw bisulfite sequencing data of the epiGBS2 run for <em>Arabidopsis thaliana.</em></p> <p>A detailed description of the laboratory protocol and an extensive manual of the bioinformatics pipeline are publicly accessible on github (<a href="https://github.com/nioo-knaw/epiGBS2">https://github.com/nioo-knaw/epiGBS2)</a> and zenodo (https://doi.org/10.5281/zenodo.4764652).</p> <p>Demultiplexed data were deposited on NCBI under the BioProject ID PRJNA764918</p>
Datasets for paper "Evaluating the PurpleAir monitor as an aerosol light scattering instrument"
<p>The data sets included will allow the user to reproduce the plots and analyses described in Ouimette et al. (2022). The Collocated*csv file contains data from multiple collocated PurpleAirs that sampled for a few days. The data in this file was used in the precision analysis in section 2.2.9 of the paper. The other files contain nephelometer and PurpleAir data from Mauna Loa (MLO) and Table Mountain (BOS) and DMPS size distribution files from BOS. Their contents are described in the README.TXT file.</p>
Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest
<p>Dataset was used for the article "Can Artificial Intelligence help in the study of vegetative growth dynamics from herbarium collections? An evaluation of the tropical flora of the French Guiana forest".</p> <p>The related work proposes to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic).</p> <p>Abstract of the paper:</p> <p>A better knowledge of tree vegetative growth patterns and their relationship to environmental variables is crucial in understanding forest growth dynamics and how climate change may affect them. Generally less studied than reproductive structures, the phenology of tree vegetative growth mainly focuses on the analysis of growing shoots, from vegetative buds development to leaf fall. This growth process usually strongly differs between temperate and tropical regions. In temperate regions, this pattern is quite well known. Low winter temperatures impose a stop of the vegetative growth shoots and lead to the typical expression of an annual growth cycle for the vast majority of tree species. In moist tropical regions, on the other hand, the seasonality is much less marked. In addition, these regions contain a much wider variety of tree species. These two aspects lead to a tremendous diversity of phenological patterns that are still poorly known and understood. In particular, not much is known on the periodicity and timing of growth at individual trees, population, or community levels.</p> <p>The work carried out in this study aims to advance knowledge in this area, focusing more particularly on herbarium scans, as herbarium collections offer the promise of monitoring plant phenology over long time periods. However, such a study requires the ability to detect a sufficiently large number of growing shoots in herbarium collections to draw statistically relevant conclusions, which can be very costly if the work is done manually. Furthermore, herbarium collections traditionally focus on reproductive organs, and herbarium specimens showing growing shoots are pretty rare.</p> <p>We propose in this paper to study to what extent the use of automated visual analysis techniques, based on deep learning, can help not only to detect these relatively rare vegetative structures in herbarium collections but also to automatically classify them by type of growing shoot (continuous or rhythmic). Our results show the relevance of using herbarium data for vegetative phenology research, as well as the potential of deep learning approaches for growth shoot detection.</p>
Perception and evaluation of (modified) wood by older adults from Slovenia and Norway (Datasets, R analysis code, and supplementary tables)
<p>This entry contains datasets, R analysis code, and supplementary tables for the article <em>Perception and evaluation of (modified) wood by older adults from Slovenia and Norway.</em></p> <p>The article investigates human perception and evaluation of handrails made of different materials. Our goal was to identify how older adults perceive handrails made of unmodified wood, modified wood, and steel. We examined if certain materials are more preferred than others, which material properties might be associated with differences in human preference, and what are the roles of tactile and tactile-visual domains in material perception. Our analysis is based on the results from an 11-item rating scale and a ranking task.</p>
Dataset for evaluation of unrealistic optimism in time of pandemic COVID-19 on a Polish sample
<p>This dataset contains the data used in a project called "Unrealistic optimism in the eye of the storm. Positive bias towards the consequences of COVID-19 during the second and third waves of the pandemic. ". The project concerns the occurrence of a cognitive bias - unrealistic optimism - with regard to contracting the coronavirus. The following information are attached to the dataset: codebooks with variables' names; analysis codes to replicate our results; supplementary materials with the description of procedures. </p>
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.