Find research datasets worth reusing

opencc-by-4.0Jul 2021View details →

Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a vertical concentrated live load

For each one of the simulations performed from the parametric analysis of masonry pointed arches with limit analysis, this database contains a .txt, a .vtk and a .png file. In the .txt file the elapsed time and the collapse multiplier of each simulation can be found. The .vtk file contains all the geometry and displacement values of every masonry panel. Finally, the .png file presents the collapse mechanism obtained.

opencc-by-4.0Jul 2021View details →

Data from the parametric analysis of masonry pointed arches with limit analysis subjected to vertical self-weight plus a proportional horizontal live load

opencc-by-4.0Sep 2021View details →

Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter : Weighted Monte Carlo samples for neutron star observables

This data release contains weighted Monte Carlo samples associated with Legred, Chatziioannou, Essick, Han, and Landry, 2021 "Impact of PSR J0740+6620 radius constraint on the properties of high-density matter" Phys. Rev. D 104, 063003; doi:10.1103/PhysRevD.104.063003

opencc-by-4.0Nov 2022View details →

caesar-mrcnn model weights

Weights files (.h5) of caesar-mrcnn source finder model at different training epochs (150, 250).

opencc-by-4.0Dec 2022View details →

Multidimensional pain profiling in people living with obesity and attending weight management services: a protocol for a longitudinal cohort study

Please note: The final dataset will not be available until data collection has been completed in the Autumn of 2024. This dataset currently contains the following: 1. Details of the study, including authorship, ethical approval, funding, registration details, an abstract for the protocol of the study and details of data being collected (both in Microsoft Word and open access .txt formats) 2. Outline of the data in the process of collection (Microsoft Excel) 3. Ethical approval letters from relevant Research Ethics Committees (PDF)     Project Title: Multidimensional pain profiling in people living with obesity and attending weight management services: a longitudinal cohort study   Authors: Keith M. Smart1,2, Natasha Hinwood1, Colin G. Dunlevy3, Catherine Doody1, Catherine Blake1, Brona Fullen1, Jean O’Connell3, Carel W. Le Roux4, Clare Gilsenan5, Francis M. Finucane6,7, Gráinne O’Donoghue1.   Corresponding author: Natasha Hinwood Address: UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland Email: <a href="mailto:natasha.hinwood@ucdconnect.ie">natasha.hinwood@ucdconnect.ie</a> Phone: +353 1 716 6511   Full name, department, institution, city, and country of all co-authors. 1UCD School of Public Health, Physiotherapy and Sport Science, University College Dublin, Dublin, Ireland 2Physiotherapy Department, St. Vincent’s University Hospital, Dublin, Ireland 3Weight Management Service, St Columcille’s Hospital, Dublin, Ireland 4Diabetes Complications Research Centre, University College Dublin, Dublin, Ireland 5Physiotherapy Department, Beaumont Hospital, Dublin, Ireland 6 School of Medicine, College of Nursing and Health Sciences, University of Galway 7Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals   ORCID 1. Keith M. Smart: 0000-0002-1598-5215 2. Natasha Hinwood: 0000-0001-9382-716X 3. Colin G. Dunlevy: 4. Catherine Doody: 5. Catherine Blake: 0000-0002-0600-629X 6. Brona Fullen: 0000-0003-4408--2063 7. Carel W. Le Roux: 0000-0001-5521-5445 8. Jean O’Connell: 0000-0001-7241-8025 9. Clare Gilsenan: 10. Francis M. Finucane: 0000-0002-5374-7090 11. Gráinne O’Donoghue: 0000-0002-9126-2094     Project abstract (Protocol): Introduction: Pain is prevalent in people living with overweight and obesity. Obesity is associated with increased self-reported pain intensity and pain-related disability, reductions in physical functioning and poorer psychological well-being. People living with obesity tend to respond less well to pain treatments or management compared to people living without obesity. Mechanisms linking obesity and pain are complex and may variously include contributions from and interactions between physiological, behavioural, psychological, socio-cultural, biomechanical, and genetic factors. Our aim is to study the multidimensional pain profiles of people living with obesity, over time, in an attempt to better understand the relationship between obesity and pain.   Methods and analysis: This longitudinal observational cohort study will recruit (n=216) people living with obesity and who are newly attending three weight management services in Ireland. Participants will complete questionnaires that assess their multidimensional biopsychosocial pain experience at baseline and at 3, 6, 12 and 18-months post-recruitment. Quantitative analyses will characterise the multidimensional pain experiences and trajectories of the cohort as a whole and in defined sub-groups.   Ethics and dissemination: The study protocol has been approved by the Ethics and Medical Research Committee of St Vincent’s Healthcare Group, Dublin, Ireland (Reference No.: RS21-059) and the University College Dublin Human Research Ethics Committee (Reference No.: LS-E-22-41-Hinwood-Smart). Findings will be disseminated through peer-reviewed journals, conference presentations, public and patient advocacy groups, and social media.

opencc-by-4.0Feb 2023View details →

Metrics As Scores Dataset: Price, Weight, and Other Properties of Over 1,200 Ideal-Cut and Best-Clarity Diamonds

This dataset is a subset of the original diamonds dataset with more than 54,000 diamonds. It was reduced to only contain diamonds of the best cut (ideal) and clarity (IF). The group is now given by the colors from J (worst) to D (best). This dataset comes from the R-package ggplot2 (Wickham 2016). For each color, we can examine the following attributes (features) of each diamond: <ul> <li>Carat: Weight of the diamond</li> <li>Depth: Total depth percentage</li> <li>Price: Price in US dollars [discrete]</li> <li>Table: Width of top of diamond relative to widest point</li> <li>X: Length in mm</li> <li>Y: Width in mm</li> <li>Z: Depth in mm</li> </ul> It has a total of 7 Colors (groups): D, E, F, G, H, I, and J. The best color is D and the worst color is J. This dataset was created to analyze whether there are differences between the colors.

opencc-by-4.0Mar 2023View details →

Sustainable Expert Criteria Weights

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. Gonzalez, J. N., Sobrino, N., & Vassallo, J. M. (2023). Considering the city context in weighting sustainability criteria for last-mile logistics solutions. International Journal of Logistics Research and Applications, 1–21. <a href="https://www.tandfonline.com/doi/full/10.1080/13675567.2023.2264788">https://doi.org/10.1080/13675567.2023.2264788</a>

opencc-by-4.0Jul 2023View details →

Input and output data (images + boulder labels, model setup, model weights and more) for the manuscript "Automatic characterization of boulders on planetary surfaces from high-resolution satellite images"

File 1: raw_data_BOULDERING.zip Size: 8.8 GB Summary: It contains all of the rasters (planetary images) and labeled boulders (raw data): <ul> <li> a boulder-mapping file, which is the manually digitized outline of boulders. </li> <li> a ROM file (stands for Region of Mapping), which depicts the image patches on which the boulder mapping has been conducted. </li> <li> a global-tiles file, which shows all of the image patches within a raster. </li> </ul> There are multiple locations/images per planetary body. Structure: <pre>. └── raw_data/ ├── earth/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif ├── mars/ │ └── image_name/ │   ├── shp/ │   │ ├── <image_name>-ROM.shp │   │ ├── <image_name>-boulder-mapping.shp │   │ └── <image_name>-global-tiles.shp │   └── raster/ │     └── <image_name>.tif └── moon/   └── image_name/     ├── shp/     │ ├── <image_name>-ROM.shp     │ ├── <image_name>-boulder-mapping.shp     │ └── <image_name>-global-tiles.shp     └── raster/       └── <image_name>.tif</pre>   File 2: best_model.zip Size: 624.7 MB Summary: This zip file contains all of the inputs and outputs required/obtained from the training of the BoulderNet Mask R-CNN model (model setup, augmentation pipeline, model weights, log during training, logged metrics): <ul> <li> augmentation_pipeline.json (required as inputs for the training of the algorithm to apply augmentations). See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information. </li> </ul> <ul> <li> Base-RCNN-FPN.yaml (base model setup file). </li> <li> config.yaml (complete model setup file, merge of the base and Mars-Moon-Earth setup file). </li> <li> Mars-MoonEarth-v050...yaml (model setup file). </li> <li> log.txt (log during training of the algorithm). </li> <li> model_0055999.pth (model weights at second last saving step) </li> <li> model_0063999.pth (model weights at last saving step) </li> </ul> We advice the use of model weights model_0055999.pth (to avoid slight overfitting). File 3: Apr2023-Mars-Moon-Earth-mask-5px.zip (pre-processed input images) Size: 252.8 MB Summary: This zip files contains the input data (images and boulder outlines) for the train, validation and test datasets. See <a href="https://github.com/astroNils">https://github.com/astroNils</a> and the MLtools repository for more information in how-to-use the different files. <ul> <li> The json folder contains json files that can be given as input (as a custom dataset) to the Detectron2 platform. The only differences between the two files is how the bounding boxes around masks have been generated. We advised to use "Apr2023-Mars-Moon-Earth-mask-5px.json". </li> <li> The pkl folder and pickle file includes some informations about the 950 image patches in our boulder dataset. </li> <li> The pre-processing folder contains all of the training, validation and test image patches and corresponding shapefiles. </li> <li> The shapefile folder is actually empty (it should not be there!). </li> </ul> Structure: <pre>. └── preprocessed_inputs/   ├── json   ├── pkl   ├── preprocessing/   │   ├── train/   │   │   ├── images   │   │   └── labels   │   ├── validation/   │   │   ├── images   │   │   └── labels   │   └── test/   │       ├── images   │       └── labels   └── shp</pre>

opencc-by-4.0Aug 2023View details →

16S V4-V5 metabarcoding reference databases and weighted naive-bayes classifiers, dereplicated

16S metabarcoding databases and naive-bayes classifiers specific to the V4-V5 region. Built from the <a href="https://www.arb-silva.de/documentation/release-138/">Silva 138.1 SSU Ref NR 99</a> database using Qiime2 (version 2023.2) and the <a href="https://github.com/BenKaehler/q2-clawback">q2-clawback plugin.</a> Includes weighted classifiers for two Earth Microbiome Project Ontology (EMPO) 3 habitat types: "sediment (saline)" and "water (saline)" , with data downloaded from <a href="https://qiita.ucsd.edu/">Qiita</a>. Sequences were dereplicated with Rescript --p-mode 'uniq' , retaining identical sequence records that have differing taxonomies. Primers used: EMP 16S 515f: GTGYCAGCMGCCGCGGTAA EMP 16S 926r: CCGYCAATTYMTTTRAGTTT Stats 286,948 unique sequences 309,567 total sequences 46,254 unique taxa (Level 7) <table> <caption>File description</caption> <thead> <tr> <th scope="col"> <table> <thead> <tr> <th>File</th> <th>Description</th> </tr> </thead> <tbody> <tr> <td>make new 16S silva V4-V5 database.md</td> <td>Markdown with code used to generate databases</td> </tr> <tr> <td>silva-138-99-seqs.qza</td> <td>Full length Silva 138.1 SSU 99 sequences</td> </tr> <tr> <td>silva-138-99-tax.qza</td> <td>Taxa for full length Silva 138.1 SSU 99 database</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-seqs.qza</td> <td>Sequences for 16S V4-V5 (primers 515f, 926r), extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible), dereplicated</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-taxa.qza</td> <td>Taxa for silva-138_1-99-515f_926r-seqs.qza database, dereplicated</td> </tr> <tr> <td>uniform-silva-138_1-99-515f_926r-uniq-classifier.qza</td> <td>Unweighted (uniform) naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-sediment-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for sediment-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-q2_2023_2-uniq-sediment-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-q2_2023_2-sediment-saline-classifier.qza</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-classifier.qza</td> <td>Weighted naive-bayes classifier for 16S V4-V5 (primers 515f, 926r) extracted from Silva 138.1 SSU 99, weighted for water-saline, generated by qiime2-2023.2 (forward compatible)</td> </tr> <tr> <td>silva-138_1-99-515f_926r-uniq-water-saline-weights.qza</td> <td>Weights used to generate silva-138_1-99-515f_926r-water-saline-classifier.qza</td> </tr> </tbody> </table> </th> <th scope="col"> </th> </tr> </thead> <tbody> </tbody> </table>

opencc-by-nc-4.0Nov 2023View details →

Model Weights for "Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting"

Model weights for use with the SatIQ fingerprinting models used in the paper “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The models are used to authenticate Iridium satellites from high sample rate message headers. The data collection and model code can be found at the following URL: <a href="https://github.com/ssloxford/SatIQ">https://github.com/ssloxford/SatIQ</a> The preprint is available on arXiv at the following URL: <a href="https://arxiv.org/abs/2305.06947">https://arxiv.org/abs/2305.06947</a> The final trained model is <code>ae-triplet-final.h5</code>. The others are from the additional experiments and analyses described in the paper, and are included for completeness. When using this data, please cite the following paper: “Watch This Space: Securing Satellite Communication through Resilient Transmitter Fingerprinting”. The BibTeX entry is given below: <pre><code>@inproceedings{smailesWatch2023, author = {Smailes, Joshua and K{\"o}hler, Sebastian and Birnbach, Simon and Strohmeier, Martin and Martinovic, Ivan}, title = {{Watch This Space}: {Securing Satellite Communication through Resilient Transmitter Fingerprinting}}, year = {2023}, publisher = {Association for Computing Machinery}, booktitle = {Proceedings of the 2023 ACM SIGSAC Conference on Computer and Communications Security}, location = {Copenhagen, Denmark}, series = {CCS '23} }</code></pre>

opencc-by-4.0Sep 2023View details →

AI-rmonizer Weights

Training results. AI-rmonizer unsupervised music composition system. Weights calculated from training with the <a href="https://magenta.tensorflow.org/datasets/maestro#v300">MAESTRO v3.0.0 MIDI dataset</a>. Curtis Hawthorne, Andriy Stasyuk, Adam Roberts, Ian Simon, Cheng-Zhi Anna Huang, Sander Dieleman, Erich Elsen, Jesse Engel, and Douglas Eck. "Enabling Factorized Piano Music Modeling and Generation with the MAESTRO Dataset." In International Conference on Learning Representations, 2019.

openCC0Jul 2022View details →

Shell length and dry tissue weight relationship of the mollusk Ostrea lurida (Olympia Oyster) in the low and subtidal zones of the San Diego River, California: 2016-2017.

Data was collected to look at the relationship between the dry tissue weight and the shell length of Ostrea lurida in the San Diego River, and use the data to compare it to other estuaries O. lurida inhabit along the west coast of North America. Sampling was done in the low and sub intertidal zones along the delta of the San Diego River from October 2016 through September 2017. A fifty meter transect was chosen to collect samples which was broken up into five 10 meter transects. In each of these 10-m transects, 0.5 X 0.5 m quadrats were randomly selected for oyster assessments. Oysters were removed by physical removal by hand. Once back in the lab shell lengths were measured and dry weights were obtained.

openCC0Oct 2023View details →

Continuous hive weight, temperature and CO2 under varying conditions of hive ventilation

CO2, a byproduct of respiration, is toxic at high concentrations so regulation of CO2 within the honey bee hive is an important colony function. In this study, we measured hive CO2 concentrations at 1-s intervals while ventilation characteristics of the hive were changed every few days, and we analyzed the data for effects of increased ventilation on colony behavior and thermoregulation. Average CO2 concentrations were significantly higher, by > 200 ppm, when hives had screened bottom boards (higher ventilation) compared to hives with solid bottom boards (lower ventilation) at the same time. Daily CO2 concentration amplitudes, hourly temperature, daily temperature amplitudes, nor hourly hive weight changes were not significantly affected by the changes in hive ventilation. In a second experiment, we found average CO2 concentrations at the top center of the upper hive box, on top of the frames, were significantly lower than concentrations at the center of a solid bottom board underneath frames, which was expected due to the higher density of CO2 relative to air. Bee colonies have been reported to cycle air, with shorter periods of 20 to 150 s and longer periods of 42–80 min, but a periodogram analysis of the CO2 concentration data found no evidence of important CO2 cycle periods other than a strong 24-h period. Bee colonies maintained strong daily cycles of CO2 concentration, with average maximum concentrations > 11,000 ppm, even in conditions of increased ventilation, indicating that managing CO2 concentration is a complex colony behavior.

openOpenApr 2016View details →

Litterfall Weights From LTER Study Site Treatment Plots: 1990 - Present

Litter is measured within the nutrient treatment plots at LTER research sites located within the Bonanza Creek Experimental Forest. The 21 LTER treatment sites monitored for litterfall represent three replicates each of four successional stages of primary succession on the floodplain of the Tanana River and three stages of succession following wildfire in the uplands. Three litter trays are placed within each treatment at the 21 LTER sites. Litter trays are 0.5 m x 0.5 m wooden frames constructed of 1" x 4" boards with fine screen stapled to the bottom side. Litter is collected in the spring as soon after snowmelt as possible (usually in May) and in some years in the fall just before freeze-up as well. Litter collections bagged are removed to the lab where they are dried in ovens at 65 deg C for at least 48 hours. Each sample is then weighed to the nearest 0. 1 grams. In 2009 treatments A and C were dropped from the study after finishing the 20 year study.

openCC0Apr 2022View details →

Dry weight biomass measurements of net-collected mesozooplankton. Samples collected in the CCE-LTER region on Process Cruises from 2006 to the present. Summaries for each Lagrangian Cycle.

Mesozooplankton are collected with plankton nets (typically a 71-cm diameter, 202-um mesh Bongo net) and samples flash frozen at sea in liquid N2 for subsequent shore-based measurements of dry weight biomass. Measurements are made by weighing pre-tared Nitex mesh on an analytical balance, for mesozooplankton size-fractionated into 5 different categories (> 0.2 mm, 0.5 mm, 1.0 mm, 2.0 mm, 5.0 mm). Biomass is expressed as dry mass of zooplankton per m3 of water filtered, or when multiplied by the maximum depth of the tow, as integrated dry mass of zooplankton per m2 of sea surface. Samples for dry weight biomass have been collected on CCE-LTER Process Cruises since 2006 and these collections are ongoing.

openCustomJan 2020View details →

Forest floor weights and Oe-Oa soil depths from 9 Hillslope Project sites in Macon County, North Carolina, within the Upper Little Tennessee River Basin

Forest floor samples were collected at 9 hillslope sites representing a gradient of development, including forested, valley agriculture, and mountain housing developments in Macon County, NC. Forest floor was divided into two categories: forest floor (i.e., Oi, Oe, & Oa layers) and wood <10 cm diameter. Afterwards, the combined depth of the Oe and Oa was measured in the middle of 3 sides of the subplot. Forest floor collections were later dried and weighed before being processed for C and N analyses.

opencc-by-4.0Nov 2018View details →

Fig. 6 in Otolith morphometry provides length and weight predictions and insights about capture sites of Prochilodus lineatus (Characiformes: Prochilodontidae)

Fig. 6. Kruskall-Wallis results of the lapillus length and weight in each Prochilodus lineatus age; ages 2 and 7 were not included in analysis because they only have one sample.

opencc-by-4.0Nov 2018View details →

Fig. 1 in Otolith morphometry provides length and weight predictions and insights about capture sites of Prochilodus lineatus (Characiformes: Prochilodontidae)

Fig. 1. Study area and location of sampling sites in the floodplain of the Upper Paraná River (Baía River – 1; Ivinhema Ri- ver – 2; Paraná River – 3; lagoa guaraná – 4; lagoa dos patos – 5; lagoa das garças – 6; lagoa do Osmar – 7; ressaco do paú véio – 8; lagoa fechada – 9; lagoa ventura – 10).

opencc-by-4.0Nov 2018View details →

Fig. 3 in Otolith morphometry provides length and weight predictions and insights about capture sites of Prochilodus lineatus (Characiformes: Prochilodontidae)

Fig. 3. Fits of the linear regressions between lapillus otolith and standard length of Prochilodus lineatus: a. Otolith Length (r2 = 0.80) and b. Otolith Weight (r2 = 0.82); the shaded area represents the confidence interval of 95% of the estimate; loess fit of raw residuals are in right panels.