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47,155 results for “Evaluation”
Lab disease outcomes data evaluating how antibiotic tolerant vs. non-tolerant cell-free supernatant from Pseudomonas aeruginosa affects the interaction between a fungal pathogen (Batrachochytrium dendrobatidis) and amphibian (Rana sylvaticus), 2022.
Microbes living on hosts and in the environment can play a key role in helping hosts to combat pathogens. However, antibiotic-induced alterations to microbial metabolite production could disrupt this dynamic. Here, we investigated whether antibiotic tolerance influences the anti-pathogenic properties of host-associated (living on the host; biofilms) and environmental (living in the soil of water column; planktonic) microbes in vitro and in vivo. For our model host and pathogen, we used the amphibian (Rana sylvatica)-Batrachochytrium dendrobatidis (Bd) system. For our model host-associated (biofilm) and environmental (planktonic) microbes, we used four strains of Pseudomonas aeruginosa that vary in their tolerance to antibiotics and their biofilm-forming capabilities: Planktonic, non-antibiotic tolerant (ΔsagS/VC); Planktonic, antibiotic tolerant (ΔsagS::sagS_L154A); Biofilm, non-antibiotic tolerant (ΔsagS::sagS_D105A); Biofilm, antibiotic tolerant (ΔsagS::sagS). We collected cell-free supernatants (CFS) from each strain to examine the effects of metabolites. We conducted four experiments. In our pathogen-only exposures to test direct effects of metabolites on Bd, we exposed Bd zoospores to each P. aeruginosa CFS at six concentrations. After 11 days of growth, we measured relative abundance of Bd across each treatment. In our host-only exposures to test effects of metabolites on host disease outcomes, we placed R. sylvatica tadpoles in individual units containing each P. aeruginosa CFS. After 48 hours, water was changed into clean well water (no CFS). Bd zoospores were immediately added to each experimental unit following the water change. After 5 days of Bd exposure, we measured snout-vent length (SVL), mass, developmental stage, and Bd quantification in the mouthparts using qPCR for each tadpole. In our host-pathogen exposures to test interactive effects of metabolites on hosts in the presence of the pathogen, we conducted the same experiment as above. However, ins
Outdoor mesocosm study evaluating how mass, NaCl tolerance, and pesticide tolerance affect oxidative stress biomarkers (CAT, SOD, GR, GPx, TBARS) in larval wood frogs (Rana sylvatica) exposed to baseline and NaCl-contaminated conditions, 2019
Biomarkers of oxidative stress can aid in wildlife monitoring by allowing conservationists to detect sublethal environmental shifts. However, interpretation of stress responses can be complicated by multiple interacting factors (e.g., individual development, evolved physiological tolerance to stressors) which alter biomarker expression. Here, we investigated how individual ontogenetic traits and population-level tolerance traits influence oxidative stress responses under baseline and contaminated environmental conditions. For our model contaminant, we used NaCl (common freshwater contaminant due to factors such as coastal flooding, irrigation, airborne salt circulation, drought, runoff from road deicing salts). For our model wildlife populations, we used larval wood frogs (Rana sylvatica) from six noninteracting populations known to vary in two population-level tolerance traits: NaCl tolerance (calculated as average time to death from lethal NaCl exposure) and pesticide tolerance (determined by proxy of distance to agriculture - a consistent and highly repeatable relationship). At an outdoor research facility, R. sylvatica tadpoles were exposed to either baseline conditions (0 g/L NaCl added) or NaCl-contaminated conditions (1 g/L NaCl added for 21 days, then reduced to 0.5 g/L NaCl). Exposures were conducted in individual units with 40 replicates per population for each treatment. The experiment was terminated per individual to capture the full term of larval development (Developmental stage: Gosner stage 36), lasting between 33-48 days. For each individual, we measured mass, Snout-Vent-Length, and developmental stage before processing for biomarker expression. Individual homogenates were assayed for oxidative stress biomarkers superoxide dismutase (SOD; responsible for Reactive Oxygen Species capture and peroxide production), glutathione peroxidase (GPx; responsible for high-affinity peroxide reduction), catalase (CAT; responsible for low-affinity peroxide reducti
Evaluation of stormwater urban ecological infrastructure in Phoenix, Arizona (USA): a case study of a small-scale bioretention basin system
In 2017, Arizona State University finished construction on a pedestrian mall central to its Tempe campus, which included a small-scale bioretention basin system for stormwater management. This study analyzed the flood control and water quality improvement performance of the small-scale bioretention basin system in the Phoenix Metropolitan Area, AZ USA. Flood control efficacy was quantified by calculating discharge from the basin system using water level loggers and measuring soil moisture levels using soil moisture probes. Stormwater runoff samples were collected for twenty-one storm events and analyzed for nitrogen and phosphorus constituent concentrations. Nutrient concentrations at the system inflow and outflow were used to determine percent change in concentration. Water quality improvement performance was compared to results from previous studies on bioretention basin system performance. These data were used to create relevant graphical figures. Results were obtained by performing statistical analysis calculations on the data measurements. The results indicated that the bioretention basin system performed adequately for flood control and water quality improvement, supporting the use of stormwater urban infrastructure systems in arid and semi-arid climates. Further research can reveal how these systems may perform during more severe storm events and offer improvements for future designs.
Evaluation of Mask R-CNN Model for Counting Reproductive Structures of Six Plant Species 1895-2018
Phenology––the timing of life-history events––is a key trait for understanding responses of organisms to climate. The digitization and online mobilization of herbarium specimens is rapidly advancing our understanding of plant phenological response to climate and climatic change. The current common practice of manually harvesting data from individual specimens greatly restricts our ability to scale data collection to entire collections. Recent investigations have demonstrated that machine-learning models can facilitate data collection from herbarium specimens. However, present attempts have focused largely on simplistic binary coding of reproductive phenology (e.g., flowering or not). Here, we use crowd-sourced phenological data of numbers of buds, flowers, and fruits of more than 3000 specimens of six common wildflower species of the eastern United States (Anemone canadensis, A. hepatica, A. quinquefolia, Trillium erectum, T. grandiflorum, and T. undulatum} to train a model using Mask R-CNN to segment and count phenological features. A single global model was able to automate the binary coding of reproductive stage with greater than 90% accuracy. Segmenting and counting features were also successful, but accuracy varied with phenological stage and taxon. Counting buds was significantly more accurate than flowers or fruits. Moreover, botanical experts provided more reliable data than either crowd-sourcers or our Mask R-CNN model, highlighting the importance of high-quality human training data. Finally, we also demonstrated the transferability of our model to automated phenophase detection and counting of the three Trillium species, which have large and conspicuously-shaped reproductive organs. These results highlight the promise of our two-phase crowd-sourcing and machine-learning pipeline to segment and count reproductive features of herbarium specimens, providing high-quality data with which to study responses of plants to ongoing climatic change.
Hydrodynamic Model Output Used to Evaluate Chinook Salmon Movements and Distribution in the South Delta
This data release includes the output variables extracted from the UnTRIM Bay-Delta hydrodynamic model (hydrodynamic model) for use in evaluating the effects of hydrodynamics on the behavior of acoustically-tagged juvenile Chinook Salmon (Oncorhynchus tshawytscha) in the Sacramento-San Joaquin Delta. Work was funded by State Water Contractors (SWC) and completed by Anchor QEA; FlowWest, LLC; and University of Washington under a SWC 2023 Science Plan grant (study name Evaluation of the Influence of State Water Project and Central Valley Project on Chinook Salmon Movements and Distribution in the South Delta), contracted by SWC. Not all the hydrodynamic model output variables in the output provided with this memorandum were used in the final fish models used to analyze Chinook Salmon responses. Model output for additional variables and locations were included for completeness and to make these output files more broadly useful to researchers interested in other locations or variables in the Sacramento-San Joaquin Delta. Hydrodynamic model simulations were conducted for 2010, 2011, 2012, 2013, 2014, 2015, 2016, and 2017, with hydrodynamic model output variables provided at mostly the same locations for each period simulated. The years 2011 through 2016 were simulated previously for a prior project and model output provided through the Environmental Data Initiative (edi.1124.1). Files for these years were recreated from the prior simulations for this project to add an output location. Additional locations were added to the 2010 and 2017 simulations for the 2010 and 2017 hydrophone arrays, and thus 2010 and 2017 include additional model output, relative to 2011 through 2016. The model simulation for each year spanned the full period of Chinook Salmon detections in the telemetry data collected during that year.
Evaluating rose yield responses to compost treatments: Data from an 18-month field study in Kenya
<p>This dataset and these scripts supports the manuscript 'Modelling cut rose yield after compost amendment over an 18-month period using repeated sigmoidal Gompertz curve fitting' by Evy de Nijs, Roland Bol, Albert Tietema & Emiel van Loon. </p> <p>Roses are an important crop for the floricultural sector of Kenya. Roses are a perennial crop and under continuous production for six to ten years. To optimize rose production, it is essential to understand how different management practices impact yield over time. This dataset contains a detailed record of rose yield data collected in an 18-month large-scale field experiment. The aim of this experiment was to evaluate the effect of pre-planting compost amendment on the yield and quality of cut roses. It was conducted in a polythene greenhouse near lake Naivasha, Kenya. Yield data included the number of stems harvested per day per flowering bed. Data presented here offer a comprehensive view of the impacts of different compost treatments on the yield of cut roses. Combined with the offered scripts, this is the framework presented in the aforementioned manuscript. This approach allows to use repeated growth curves to analyze yields compared to a baseline General Additive Model. </p> <p> </p> <p>de Nijs, E. A., Tietema, A., Bol, R., & van Loon, E. E. (2025). Modeling Cut Rose Yield Over an 18‐Month Period After Compost Amendment Using Repeated Sigmoidal Gompertz Curve Fitting. <em>Plant‐Environment Interactions</em>, <em>6</em>(3), e70049.</p>
Dataset for "Methodology of Evaluating the Activation Energy of Oxygen Reduction Reaction on Pt-based Electrodes"
<p>High temperature proton-exchange membrane fuel cell (HT-PEMFC) technology is widely studied alternative to current energy conversion technologies based on fossil fuels. Compared to solid oxide fuel cells (SOFCs), HT-PEMFCs allow more flexibility and demand less operation control due to their lower temperature. On the other hand, HT-PEMFCs show an advantage over low-temperature PEMFCs in terms of less demand on the purity of the H2 used, the possibility to recover the generated heat, lower water management requirements, and easy heat management. One of the critical limitations of HT-PEMFC operation is a slow kinetics of the cathodic reduction of O2 (ORR) due to presence of H3PO4 which ensures proton conductivity in the system. Electrochemical dynamic methods such as cyclic voltammetry or linear sweep voltammetry (LSV) can be used to determine the kinetic parameters of ORR. These measurements can provide information on the Tafel slope and exchange current density (jex) of the ORR. However, performing these measurements under conditions relevant for HT-PEMFC operation is challenging due to presence highly concentrated H3PO4 and elevated temperature. First, determination of the kinetic parameters requires correct assessment of equilibrium potential of ORR (EORR). The value of the EORR is generally influenced by the activity (fugacity) of the reactants and products and the temperature, a discussion of the appropriate standard states of the components is also necessary. Second, the relationship between the jex and the reaction rate constant (k°), necessary for calculation of activation energy ( ), must be known. It includes consideration of the likely reaction mechanism. In this paper, the methodology for appropriate determination of was developed and used for estimation of of ORR from LSV curves measured on commercially available Pt/C catalyst under HT-PEMFC relevant conditions. In particular, the measurements were carried out using a rotating glassy carbon rod disk electrode (RRE) in purified 98 wt.% H3PO4 (as electrolyte) at temperatures of 120, 140, 160, 180 °C. Though the treatment was developed in context of ORR and HT-PEMFC, the approach is generally applicable to any electrochemical reaction.</p>
EOSC Task Force on FAIR Metrics and Data Quality: FAIR Evaluation community survey 2023
<p>The EOSC-A FAIR Metrics and Data Quality Task Force (TF) supported the European Open Science Cloud Association (EOSC-A) by providing strategic directions on FAIRness (Findable, Accessible, Interoperable, and Reusable) and data quality. The Task Force conducted a survey using the <a href="https://ec.europa.eu/eusurvey/">EUsurvey tool</a> between 15.11.2022 and 18.01.2023, targeting both developers and users of FAIR assessment tools. The survey aimed at supporting the harmonisation of FAIR assessments, in terms of what it evaluated and how, across existing (and future) tools and services, as well as explore if and how a community-driven governance on these FAIR assessments would look like. The survey received 78 responses, mainly from academia, representing various domains and organisational roles. This is the anonymised survey dataset in csv format; most open-ended answers have been dropped. The codebook contains variable names, labels, and frequencies.</p>
Data associated with the following publication: Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel
<p>This data set contains the supporting data associated with the following publication:</p> <p>Morgenthaler, T., Loebach, J., Lynch, H., Pentland, D., Kottorp, A., & Schulze, C. (in press). Developing the Playground Play Value and Usability Audit Tool (PVUA): An Evaluation of Content Validity via an Expert Panel. Children, Youth and Environments. [DOI was not yet available when the data set was published]</p> <p>The data set includes the following files:</p> <ul> <li>read me file [contains all relevant information to understand and reuse this data set] </li> <li>13 additional files [for description, see read me file]</li> </ul> <p>For more information, please contact the lead researcher, Thomas Morgenthaler (tom.morgenthaler@gmail.com or 121101888@umail.ucc.ie)</p> <p> </p>
Classes of errors in DOI names: evaluation dataset
<p>This dataset contains the results of the evaluation of the methodology presented in the article <em>Identifying and correcting invalid citations due to DOI errors in Crossref data</em> (<a href="https://arxiv.org/abs/2111.11263">https://arxiv.org/abs/2111.11263</a>).</p> <p>The file named 10_random_citations_per_rule.csv contains 193 randomly selected citations from the corrected citations obtained by the process described in the article (<a href="https://doi.org/10.5281/zenodo.4892551">10.5281/zenodo.4892551</a>). They were extracted using the script called evaluation.py, which can be viewed in the GitHub repository <em>open-sci/2020-2021-grasshoppers-code </em>(<a href="https://doi.org/10.5281/zenodo.4723983">10.5281/zenodo.4723983</a>).</p>
QuaLiKiz-v2.6.2 turbulent transport model evaluations based on JET experimental plasma profiles
<p>This dataset was used to train the QuaLiKiz-neural-network (QLKNN) model, QLKNN-jetexp-15D, described within the following published article: <a href="https://doi.org/10.1063/5.0038290">https://doi.org/10.1063/5.0038290</a>. It was generated with approximately 33 million standalone evaluations of QuaLiKiz-v2.6.2, each performed with a standard vector of 18 wavenumbers. Only approximately 21 million of these are kept for training due to various consistency checks applied to the code outputs. More information about the QuaLiKiz code can be found at <a href="https://www.qualikiz.com">www.qualikiz.com</a>.</p> <p>The data is saved under 3 keys in HDF5 format: "/input", "/output", and "/label". The inputs to the QuaLiKiz evaluations are provided under "/input", representing the local plasma parameters extracted from experimental measurements from the JET plasma device in Culham, UK, along with variations of select parameters according to propagated experimental uncertainties. Selected relevant outputs of the QuaLiKiz evaluations are provided under "/output", namely the local turbulent transport coefficients after applying a semi-empirical turbulent fluctuation saturation rule. Some useful metadata is provided under "/label", giving some degree of provenance tracking back to the JET experimental database, as well as describing the applied parameter variations and explaining why certain output rows were removed from the output structure.</p>
Dataset for Towards improved online dissolution evaluation of Pt-alloy PEMFC electrocatalysts via electrochemical flow cell - ICP-MS setup upgrades
<p>Experimental data comprises raw data from ICP-MS (Inductively coupled plasma mass spectrometry) (i.e. time dependence of signal intensity for Co59 and Pt195) for different cell geometry and operating parameters. <br>Model data comprise of time- and space-dependent values of Pt ions concentration in the modelling cell and local velocity vectors.</p>
An Empirical Evaluation of the Relationship between Technical Debt and Software Security
<p>This dataset contains the static analysis results of 50 open source software applications retrieved from Github. The results were produced by using SonarQube, PMD, CKJM Extended and Findbugs tools. Further details can be found in the relevant publication: </p> <ul> <li>Siavvas, M., Tsoukalas, D., Janković, M., Kehagias, D., Chatzigeorgiou, A., Tzovaras, D., Aničić, N., Gelenbe, E. <em>An Empirical Evaluation of the Relationship between Technical Debt and Software Security</em>. In: Konjović, Z., Zdravković, M., Trajanović, M. (Eds.) ICIST 2019 Proceedings Vol.1, pp.199-203, 2019</li> </ul>
Datasets for evaluating scalable supervised learning for synthesize-on-demand chemical libraries
<p>This repository contains datasets for the manuscript "Evaluating scalable supervised learning for synthesize-on-demand chemical libraries":</p> <ul> <li><strong>ams_all_preds.csv.gz</strong>: The AMS dataset predictions when using an RF or baseline model trained on the training dataset. Includes the predicted score and rank from each model for each compound. We started with 8,434,707 AMS compounds and detected that 247,025 were in the LC or MLPCN training data. These were removed from the AMS list, leaving 8,187,682 compounds to score. The compound matching was done on the SMILES that we canonicalized in rdkit.</li> <li><strong>ams_order_results.csv.gz</strong>: Information about the 1,024 compounds purchased from the AMS library. Excludes the 4 AMS compounds that were incompletely dissolved. Includes the chemical feature representation, information from the vendor, RF and baseline model predictions, screening results, and clustering results.</li> <li><strong>baseline_weight.npy</strong>: The saved Similarity Baseline model, which consists of the active compounds in the training data. This model was used to score the AMS library. See the <a href="https://github.com/gitter-lab/pria-ams-enamine">GitHub repository</a> for code to load the model and make predictions on new compounds.</li> <li><strong>cdd_training_data.tar.gz</strong>: The LC1234 and MLPCN PriA-SSB screening data exported from CDD.</li> <li><strong>enamine_costs_clustered_v3_with_nneighbor.csv.gz</strong>: Contains 5,620 Enamine compounds that were selected based on the RF prediction score and availability. This file also contains the Taylor-Butina cluster ID when clustering the training compounds, 1,024 tested AMS compounds, and top-ranked Enamine compounds at a 0.4 threshold. The nearest neighbor compounds in the training and AMS sets are also included along with compound information from Enamine, RF model scores, and chemical feature representations.</li> <li><strong>enamine_dose_response_curve_plots.xlsx</strong>: Images of the dose response curves from all three runs on the 68 Enamine compounds. If a compound was tested multiple times, multiple curves are shown in the same plot. The compound structure images and SMILES are exported from CDD, not generated with RDKit.</li> <li><strong>enamine_dose_response_curves.tsv</strong>: The dose response curve summaries from all three runs on the 68 Enamine compounds. If a compound was tested multiple times, only the highest-quality dose response curve was used.</li> <li><strong>enamine_final_list.csv.gz</strong>: The final 100 filtered compounds from <code>enamine_top_10000.csv.gz</code>. Contains compound information from Enamine as well as RF model scores, chemical feature representations, and clustering results.</li> <li><strong>enamine_PriA-SSB_dose_response_data.tar.gz</strong>: The dose response screening data from all three runs on the 68 Enamine compounds. The 2021-06-16 run was originally screened on 2020-08-24. 2021-06-16 is the date the compound identities were corrected. This run contains two 1,536 well plates.</li> <li><strong>enamine_top_10000.csv.gz</strong>: Top 10,000 predictions from the Enamine REAL dataset using the selected RF model. Contains compound information from Enamine as well as RF model scores, chemical feature representations, and clustering results.</li> <li><strong>master_df.csv.gz</strong>: The output of preprocessing the files in <code>cdd_training_data.tar.gz</code>. Contains 441,900 rows.</li> <li><strong>random_forest_classification_139.pkl</strong>: The saved RF classification model with hyperparameter ID 139. This model was used to score the AMS and Enamine REAL libraries. See the <a href="https://github.com/gitter-lab/pria-ams-enamine">GitHub repository</a> directory for code to load the model and make predictions on new compounds.</li> <li><strong>train_ams_real_cluster.csv.gz</strong>: Contains cluster IDs for Taylor-Butina clustering at a 0.4 threshold applied to the training compounds, 1,024 tested AMS compounds, and top-ranked compounds from Enamine. Includes the chemical features, dataset to which the compound belongs, leader compound for each cluster, and whether the compound is a known hit.</li> <li><strong>training_df_single_fold.csv.gz</strong>: This is all ten folds in <code>training_folds.tar.gz</code> merged for convenience. Contains 427,300 compounds.</li> <li><strong>training_df_single_fold_with_ams_clustering.csv.gz</strong>: Contains cluster IDs for Taylor-Butina clustering applied to the 427,300 training compounds and the 1,024 tested AMS compounds. Different clustering results are shown at the 0.2, 0.3, and 0.4 thresholds. Includes the leader compound for each cluster. Although the training and AMS compounds were clustered jointly, only the training compounds' clusters are shown. The AMS compounds' clusters are in <code>ams_order_results.csv.gz</code>.</li> <li><strong>training_folds.tar.gz</strong>: The LC1234 and MLPCN training data split into ten folds. This dataset with 427,300 compounds was used for cross validation and model selection. This dataset is derived from <code>master_df.csv.gz.</code></li> </ul> <p>If you use these datasets in a publication, please cite:</p> <p>Moayad Alnammi, Shengchao Liu, Spencer S. Ericksen, Gene E. Ananiev, Andrew F. Voter, Song Guo, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. <a href="https://doi.org/10.1021/acs.jcim.3c00912">Evaluating scalable supervised learning for synthesize-on-demand chemical libraries</a>. <em>Journal of Chemical Information and Modeling</em> 2023.</p> <p>See PubChem AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1272365">1272365</a>, AID <a href="https://pubchem.ncbi.nlm.nih.gov/bioassay/1918986">1918986</a>, and the associated publications for details about the PriA-SSB screening data. The screening datasets were compiled from three separate sources that should all be cited if the training dataset is used in a publication:</p> <ul> <li>Moayad Alnammi, Shengchao Liu, Spencer S. Ericksen, Gene E. Ananiev, Andrew F. Voter, Song Guo, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. <a href="https://doi.org/10.1021/acs.jcim.3c00912">Evaluating scalable supervised learning for synthesize-on-demand chemical libraries</a>. <em>Journal of Chemical Information and Modeling</em> 2023.</li> <li>Shengchao Liu<sup>+</sup>, Moayad Alnammi<sup>+</sup>, Spencer S. Ericksen, Andrew F. Voter, Gene E. Ananiev, James L. Keck, F. Michael Hoffmann, Scott A. Wildman, Anthony Gitter. <a href="https://doi.org/10.1021/acs.jcim.8b00363">Practical model selection for prospective virtual screening</a>. <em>Journal of Chemical Information and Modeling</em> 2018.</li> <li>Andrew F. Voter<sup>+</sup>, Michael P. Killoran<sup>+</sup>, Gene E. Ananiev, Scott A. Wildman, F. Michael Hoffmann, James L. Keck. <a href="https://doi.org/10.1177/2472555217712001">A high-throughput screening strategy to identify inhibitors of SSB protein–protein interactions in an academic screening facility</a>. <em>SLAS Discovery</em> 2018.</li> </ul> <ul> </ul>
Harmful algal bloom and aquatic weeds data from the Sacramento-San Joaquin Delta, collected to evaluate the impact of the 2021 Temporary Urgency Change Order and Emergency Drought Barrier
Condition 8 of the June 2021 Temporary Urgency Change Order for the Central Valley Project (CVP) and State Water Project (SWP) requires a special study of harmful algal blooms (HABs) in the Sacramento–San Joaquin Delta (Delta) and the spread of submersed aquatic vegetation (SAV), and floating aquatic vegetation (FAV), also referred to as “aquatic weeds”. A report on the study was submitted to the State Water Resources Control Board on June 1, 2022. This data package contains all publicly available data used in the report, including visual cyanobacteria reports, cyanotoxin data, water quality, nutrients, flow/hydrodynamics, chlorophyll-a concentrations, temperature, coverage of SAV and FAV, use of herbicides, and human populations. Many of these data were derived from other datasets, though some were collected specifically for this study
Greenhouse mixed culture experiment from August 2002 to April 2003 (FCE): Evaluate the effect of salinity and hydroperiod on interspecific mangrove seedlings growth rate (mixed culture) / Morphometric variables
A greenhouse experiment (mixed culture experiment) was performed for 8 months to evaluate the effect of salinity and hydroperiod on seedling growth rates of 2 mangrove species( Laguncularia racemosa and Rizhophora mangle). Data analyses are currently being performed.
Data from Bieri Thesis: Evaluating Coastal Protection Benefits of Restored Oyster Reef Designs - 2022
This dataset consists of spreadsheets used in the creation of: "Elizabeth Bieri, Evaluating Coastal Protection Benefits of Restored Oyster Reef Designs. MS Thesis, University of Virginia, Charlottesville, VA. Advisors: Matthew Reidenbach & Patricia Wiberg, 2022" (https://doi.org/10.18130/2b04-wz72). Documentation on methods and types of data are given in the thesis and are not repeated here. There are two .zip files. One contains the original Excel workbooks. The other contains the data from individual sheets within the workbooks as comma-separated-value (.csv) text files. The file listing for the CSV files are: Archive: Bieri_Comma_separated_Value_Files.zip Length Date Time Name --------- ---------- ----- ---- 0 2025-12-15 12:47 Bieri_Comma_separated_Value_Files/ 1787 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_crests.csv 239 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_S4.csv 273 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/elevation_S7.csv 652 2025-12-15 12:45 Bieri_Comma_separated_Value_Files/erosionpins_exposed.csv 476 2025-12-15 12:45 Bieri_Comma_separated_Value_Files/erosionpins_exposed_no_reef.csv 102 2025-12-15 12:46 Bieri_Comma_separated_Value_Files/erosionpins_S4.csv 97 2025-12-15 12:46 Bieri_Comma_separated_Value_Files/erosionpins_S7.csv 297 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/grainsize_july.csv 290 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/grainsize_oct.csv 399 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_All.csv 509 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_July2021.csv 507 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Oct2021.csv 1242 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Sed_OM.csv 516 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/infauna_om_Sept2020.csv 913981 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/jul2021waveS4.csv 715881 2025-12-15 12:41 Bieri_Comma_separated_Value_Files/jul2021waveS7.csv 5251
MiRoR2 - P1 - Shortcomings in the evaluation of biomarkers in ovarian cancer: a systematic review
<p>Data set for the study “Shortcomings in the evaluation of biomarkers in ovarian cancer: a systematic review”, including search strategy, extraction form, extracted data with summary of results, and protocol</p>
Dataset used for evaluation of GRASP-AOD algorithm
<p><strong>Dataset used for evaluation of GRASP-AOD algorithm</strong></p> <p>30 AERONET sites where processed using GRASP v1.0.0 and following the methodology described in Torres et. al 2017 and Torres et Fuertes 2020. More sites and data can be found at <a href="http://www.grasp-open.com">www.grasp-open.com</a> .</p> <p>31 files can be found. 30 Files described the 30 AERONET sites used for GRASP-AOD validation while the file aureole_Granada.csv is used in the section 4.2 for GRASP-AUR test.</p> <p>Description of the columns that can be found in the datafiles:</p> <ul> <li>date</li> <li>FineModeMedianRadius</li> <li>FineModeGeometricStandardDeviation</li> <li>FineModeVolumeConcentration</li> <li>CoarseModeMedianRadius</li> <li>CoarseModeGeometricStandardDeviation</li> <li>CoarseModeVolumeConcentration</li> <li>VolumeConcentration</li> <li>Effective radius</li> <li>abs_error</li> <li>rel_error</li> <li>aod500_fine</li> <li>aod500_coarse</li> <li>aod380_retrieved</li> <li>aod440_retrieved</li> <li>aod500_retrieved</li> <li>aod870_retrieved</li> <li>aod1020_retrieved</li> <li>input380nm</li> <li>input440nm</li> <li>input500nm</li> <li>input870nm</li> <li>input1020nm</li> <li>exist_380nm</li> <li>exist_440nm</li> <li>exist_500nm</li> <li>exist_870nm</li> <li>exist_1020nm</li> <li>number_of_wavelengths</li> <li>min_wavelength</li> <li>max_wavelength</li> <li>climatology_method</li> <li>wavelengths_used</li> </ul> <p><strong><em>Please follow the data policy of each data source:</em></strong></p> <p>GRASP-AOD: GRASP-OPEN (<a href="https://www.grasp-open.com/products/">https://www.grasp-open.com/products/</a>)</p> <p>AERONET: <a href="http://www.aeronet.gsfc.nasa.gov/">http://www.aeronet.gsfc.nasa.gov</a></p> <p>Details can be found in manuscript:</p> <p>Torres B. et Fuertes D., Characterisation of aerosol size properties from measurements of spectral optical depth: a global validation of the GRASP-AOD code using long-term AERONET data, sent to review on 2020</p>
Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems - Image Dataset
<p>This dataset contains data used in the research published by MLabs Optronics in the paper:</p> <p>Medina Heierle, Victor, María Tejada Casado, Alberto Briasco González, Hugo Jestes Zoilo, Jesús Martín Tapia, Adeodato Altamirano Aguilar, and Javier Muñoz De Luna Clemente. Evaluation Framework for Multiband Image Enhancement and Blending Algorithms in Enhanced Flight Vision Systems. Proceedings of the 14th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS), pp. 274-280. IEEE, 2018.</p> <p><br> The dataset is classified into 3 folders:</p> <p>- IR_VIS: Contains 28 pairs of images in the IR (some images may be in the NIR spectrum instead) and Visual spectrum, taken from different public repositories off the internet, which are typically used in multispectral fusion research.<br> <br> - Fusion: Contains 8 sets with the results of applying each of the 4 fusion algorithms described in the paper on some of the images in folder "IR_VIS".</p> <p>- VIS haze filtering: Contains 24 images taken with a CCD camera of a contrast target inside a fog simulation cabin in a laboratory. For comparison purposes, all images have been taken with a similar amount of fog, which is as much as was possible while still being able to see the target with the camera through the fog. Each image has been taken with a different type of filter (filter information is provided in another image inside the folder).</p> <p> </p> <p>Mlabs Optronics<br> PTA<br> Calle Pierre Laffitte, 8<br> 29590 Málaga (Spain)</p> <p>www.mlabsoptronics.com<br> info@mlabsoptronics.com</p>
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Allen Brain Atlas
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OpenNeuro
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