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7,503 results for “methods”
Data: Methods for tagging an ectoparasite, the salmon louse Lepeophtheirus salmonis
<p>Monitoring individuals within populations is a cornerstone in evolutionary ecology, yet<span> </span>individual tracking of invertebrates and particularly parasitic organisms remains rare. To address this gap, we describe here a method for attaching radio frequency identification<span> </span>(RFID) tags to individual adult females of a marine ectoparasite, the salmon louse<span> </span><em><span>Lepeophtheirus salmonis</span></em>. Comparing two alternative types of glue, we found that one of them<span> </span>(2-octyl cyanoacrylate, <em><span>2oc</span></em>) gave a significantly higher tag retention rate than the other (ethyl<span> </span>2-cyanoacrylate, <em><span>e2c</span></em>). This glue comparison test also resulted in a higher loss rate of adult ectoparasites from the population where tagging was done using <em><span>2oc</span></em>, but this included males<span> </span>not tagged and thus could also suggest a mere tank effect. Corroborating this, a more extensive analysis using data collected over two years showed no significant difference in<span> </span>mortality after repeated exposure to the <em><span>2oc </span></em>glue, nor did it show any significant effect of the<span> </span>tagging procedure on the reproduction of female salmon lice. The proportion of RFID-tagged<span> </span>individuals followed a negative exponential decline, with tag retention among the living<span> </span>female population generally high. The projected retention was found to be about 88% after<span> </span>30 days or 80% after 60 days, although one of the four batches of glue used, purchased from<span> </span>a different supplier, appeared to give significantly lower tag retention and with greater initial<span> </span>loss (74% and 60% respectively). Overall, we find that RFID tagging is a simple and effective technology that enables documenting individual life histories for invertebrates of a suitable size, including marine and parasitic species, and that it can be used over long periods of study.</p>
Two datasets to illustrate quantitative analysis methods for fluorescent calcium measurements
<p>Two datasets in HDF5 formats used for illustrating some quantitative data analysis methods.</p> <p><strong>CCD_calibration.hdf5</strong>: Imago/SensiCam CCD camera (Till Photonics) calibration data set. <br> Fluorescence measurments were made using a fluorescent plastic slide. 10 exposure times from 10 to 100 ms (each making an HDF5 group) were used. For each exposure time 100 exposures were performed (with 200 ms between each). The fluorescence measured in each of the 60 x 80 pixels of the camera are stored in the stack data set of each group. The time data set (a vector) of each group contains the time at which each illumination was done. These recordings were done by Andreas Pippow (Kloppenburg Laboratory Cologne University, http://cecad.uni-koeln.de/Prof-Peter-Kloppenburg.82.0.html). <br> They were used in: Sébastien Joucla, Andreas Pippow, Peter Kloppenburg and Christophe Pouzat (2010) Quantitative estimation of calcium dynamics from ratiometric measurements: A direct, non-ratioing, method. Journal of Neurophysiology 103: 1130-1144.</p> <p><strong>Data_POMC.hdf5</strong>: POMC data set recorded by Andreas Pippow (Kloppenburg Laboratory Cologne University, http://cecad.uni-koeln.de/Prof-Peter-Kloppenburg.82.0.html). 168 measurements performed with a CCD camera recording Fura-2 fluorescence (excitation wavelength: 340 nm). The size of the CCD chip is 60 x 80 pixels. A stimulation (depolarization induced calcium entry) comes at time 527. <br>Details about this data set can be found in: Joucla et al (2013) Estimating background-subtracted fluorescence transients in calcium imaging experiments: A quantitative approach. Cell Calcium. 54 (2): 71-85.</p> <p> </p>
OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods
<p>Optical coherence tomography (OCT) is a non-invasive imaging technique that has extensive clinical applications in ophthalmology. OCT enables the visualization of the retinal layers, playing a vital role in the early detection and monitoring of retinal diseases. OCT uses the principle of light wave interference to create detailed images of the retinal microstructures, making it a valuable tool for diagnosing ocular conditions. Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods (OCTDL) comprising over 2000 OCT images labeled according to disease group and retinal pathology.</p> <p>The dataset consists of the following categories and images:<br>- Age-Related Macular Degeneration - 1231 images;<br>- Diabetic Macular Edema - 147 images;<br>- Epiretinal Membrane- 155 images;<br>- Normal - 332 images;<br>- Retinal Artery Occlusion - 22 images;<br>- Retinal Vein Occlusion - 101 images;<br>- Vitreomacular Interface Disease - 76 images.</p> <p>This dataset is published to provide researchers and developers with access to a large set of labeled images, which contributes to the development and improvement of algorithms for the automatic processing and analysis of OCT images for early diagnosis and monitoring of eye diseases. CSV file consists of file_name, disease, subcategory, condition, patient_id, eye, sex, year, image_width, and image_height. The dataset will be updated periodically.</p> <p> </p> <p>For more information and details about the dataset see:</p> <p>https://rdcu.be/dELrE</p> <p>https://arxiv.org/abs/2312.08255</p> <pre>@article{kulyabin2024octdl, title={OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods}, author={Kulyabin, Mikhail and Zhdanov, Aleksei and Nikiforova, Anastasia and Stepichev, Andrey <br> and Kuznetsova, Anna and Ronkin, Mikhail and Borisov, Vasilii and Bogachev, Alexander <br> and Korotkich, Sergey and Constable, Paul A and Maier, Andreas}, journal={Scientific Data}, volume={11}, number={1}, pages={365}, year={2024}, publisher={Nature Publishing Group UK London},<br> doi={https://doi.org/10.1038/s41597-024-03182-7} } </pre>
"Is Heidi really happier in the mountains? A mixed-methods investigation of spatial affect in fiction." - Data
<p>This repository provides access to the data used in Grisot, G & Herrmann, J. B. (2024) "Is Heidi really happier in the mountains? A mixed-methods investigation of spatial affect in fiction"</p> <p>It contains the following datasets:</p> <ul> <li><a href="https://zenodo.org/api/records/14235844/draft/files/all_entities.csv/content" target="_blank" rel="noopener noreferrer">all_entities.csv</a>: the spatial entities lists used in the paper (see Grisot, G & Herrmann, J. B., 2023)</li> <li><a href="https://zenodo.org/api/records/14235844/draft/files/corpus_books_aggr_sent_norm.csv/content" target="_blank" rel="noopener noreferrer">corpus_books_aggr_sent_norm.csv</a>: a corpus of N=184 Swiss literary narrative texts written in German between 1822 and 1940 by 69 Swiss authors, with sentiment values and spatial entities identified in each sentence.</li> <li><a href="https://zenodo.org/api/records/14235844/draft/files/heidi_clean_aggr_sent.csv/content" target="_blank" rel="noopener noreferrer">heidi_clean_aggr_sent.csv</a>: the 1880 digitised edition of the novel <em>Heidi</em>, as available from E-Rara, with sentiment values and spatial entities identified in each sentence.</li> <li><a href="https://zenodo.org/api/records/14235844/draft/files/sentiart.csv/content" target="_blank" rel="noopener noreferrer">sentiart.csv</a>: the sentiment lexcon SentiArt (Jacobs, 2019)</li> </ul>
Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures
<p>This Dataset comprises two sub-sets of information:</p> <ul> <li>Database and Results of the work present in the paper "Robust Method for Property Prediction via Artificial Neural Networks: Incorporating Key Structural Features for Carbon Dioxide – Ionic Liquid Mixtures" published in The Journal of Physical Chemistry B (https://doi.org/10.1021/acs.jpcb.4c04432).</li> <li>Sample of the code used, in order to reproduce any of the results presented above. This can be found in the previous version of this Dataset (v1.0 https://zenodo.org/records/11216901)</li> </ul> <p> </p> <p>Regarding the sample code, an example for all ANN Models used in this work is provided. This includes the three models used:</p> <ol> <li>One based only on Critical Properties of Ionic Liquids (CRT Model)</li> <li>One based only on Structural Properties of Ionic Liquids (STR Model)</li> <li>One combination of the previous models, taking into account both Critical and Structural Properties (COMB Model)</li> </ol> <p>In this manner, it is possible to observe the differences between the performance of the different models, either through statiscal analysis or using graphical representation. This allows for the benchmarking to be done in a more concise way.</p>
Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river"
<h2>Summary</h2> <p>Dataset related to the publication "Temporal dynamics and environmental controls of carbon dioxide and methane fluxes measured by the eddy covariance method over a boreal river" by Aki Vähä, Timo Vesala, Sofya Guseva, Anders Lindroth, Andreas Lorke, Sally MacIntyre, and Ivan Mammarella (2024), published in Biogeosciences.</p> <h2>Materials and Methods</h2> <h3>Measurement site</h3> <p>The experiment was conducted on a floating platform on the River Kitinen in northern Finland. The measurements took place from 1 June to 2 October, 2018.</p> <p>The River Kitinen is 235 km long and has a catchment area of 7672 km2. The catchment area consists mostly of managed boreal forest with Scots pine (Pinus sylvestris) and Norway spruce (Picea abies) as the main tree species, wetlands of which a large portion is drained, small streams and rivers, some low mountains and a few small settlements. The experiment site (67.37◦ N, 26.62◦ E, 173 m above sea level) was located next to the Finnish Meteorological Institute’s research and weather station in Tähtelä. At the experiment location the river is 180 m wide and forms a straight section extending approximately 600 m upstream and 1000 m downstream from the site. The direction of the river at the site is roughly north-northwest–south-southeast and it flows towards the south. The mean annual discharge, measured at the closest power plant downstream, is 103 m3 s−1. The maximum depth at the site is 7 m. The River Kitinen’s Strahler stream order at the site is 5. The floating platform was located about 70 m from the eastern river bank where the water depth was 4.5 m.</p> <h3>Eddy covariance</h3> <p>The eddy covariance system measuring water-atmosphere turbulent fluxes was mounted on a mast on the southern side of the platform. This installation consisted of an ultrasonic anemometer (uSonic-3 Scientific, METEK Meteorologische Messtechnik GmbH, Elmshorn, Germany) for measuring the wind speed in three Cartesian coordinates and the sonic temperature, an enclosed-path gas analyser (LI-7200RS, LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) for measuring carbon dioxide and water vapour mole fractions, and a closed-path gas analyser (G1301-f, Picarro, Inc., Santa Clara, California, USA) for measuring methane and water vapour mole fractions. The centre of the sonic anemometer was 1.82 m above the water surface. An inclinometer (DOG2 micro-electro-mechanical system, Measurement Specialties, Inc., Hampton, Virginia, USA) was used for measuring the pitch and roll of the platform. Eddy covariance fluxes were calculated using the EddyUH software (Mammarella et al. 2016), following the state of art methodologies (Sabbatini et al. 2018, Nemitz et al. 2018).</p> <h3>Auxiliary measurements</h3> <p>Ambient air temperature and relative humidity were measured with a Rotronic HC2-S3C03 probe (Rotronic AG, Bassersdorf, Germany), mounted inside a Young model 41003 (R. M. Young Company, Traverse City, Michigan, USA) multi-plate radiation shield on the platform’s north-eastern corner. Air temperature and relative humidity were available only after 15th of June. Before that, the sonic temperature and humidity calculated from χH2O, measured with the LI-7200RS, were used instead. Atmospheric pressure and precipitation were measured at the Tähtelä weather station. Photosynthetically active radiation (PAR) in water was measured with two LI-192 sensors (LI-COR Biosciences, Inc., Lincoln, Nebraska, USA) and one LI-193 sensor (LI-COR). The sensors were hanging from wires at 0.3 m, 0.65 m and 1.0 m depths on a beam on the southern side of the platform. Measurements of water side CO2 partial pressure (pCO2) were done by using an off-axis integrated cavity output spectrometer (Ultraportable Greenhouse Gas Analyzer – UGGA), Los Gatos Research, Inc., Santa Clara, California, USA) that was connected to the headspace of an equilibrator consisting of a floating Plexiglas chamber.</p> <p>A water temperature chain was set up 100 m upstream of the platform. It consisted of five temperature loggers of the type RBR Solo (RBR Ltd. Ottawa, Ontario, Canada). The loggers were placed on a taut line mooring at depths of 0.35 m, 1.35 m, 2.35 m, 3.35 m and 4.35 m (6 June to 17 June) and 0.07 m, 1.05 m, 2.05 m, 3.05 m and 4.05 m (17 June onwards). The topmost measurement was used as the surface temperature. The water flow velocity was measured with a acoustic Doppler velocimeter (Nortek Vector, Nortek AS, Rud, Norway) which was installed on a beam on the north-western corner of the platform, facing down (Guseva et al., 2021). The depth of the measurements was 0.4 m below the surface.</p>
Students' perceived obstacles with Forced Online Distance Learning during the CoVID-19 outbreak and their preferences to continue with the introduced teaching methods after the reopening of the University of Maribor [Project documentation]
<p>The outbreak of COVID -19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID -19 epidemic and started the first project with the working title: The Side Effects of Forced Online Distance Education (FODE).</p> <p>The aim of the second study, conducted during the first wave of the epidemic in March 2020, was to investigate the response of university students to the new situation. The project documentation provided for the Forced Online Distance Learning (FODL) consists of:</p> <ul> <li>abstract,</li> <li>instrument,</li> <li>copy of the descriptive statistics,</li> <li>and SPSS dataset.</li> </ul>
Metrics and Methods used in IEEE/ACM HRI and IEEE RoMan Conference Proceedings from 2015 to 2021 Data
<p>This work analyzes a total of 1464 papers through examination of seven years of HRI Conference proceedings(2015 through 2021) and six years of Ro-Man Conference proceedings (2015 through 2020) to present a holistic snapshot of the state of methods and metrics in HRI research. Workshop proposals, late-breaking results, keynote talk abstracts, and demo presentations were omitted from this study due to varying methodologies and shorter study timeframes.</p>
Mixed methods systematic review and metasummary about barriers and facilitators for the implementation of cotrimoxazole and isoniazid - preventive therapies for people living with HIV.
<p>This is the minimal data set underlying the findings of our systematic review and metasummary:</p> <p>We uploaded the following data extracted from the studies included in our review:</p> <p>- Systematic Review protocol, also published in PROSPERO (CRD42019137778).</p> <p>- detailed description of studies included in our review.</p> <p>- barriers identified in the review (metasummary).</p> <p>- facilitators identified in the review.</p>
Effects of the type of lunch meal and teaching-learning methods on CRI student engagement in afternoon classes.
<p><br> The post-lunch slump is a natural dip in energy caused not only by our circadian cycles but also as a result of digestive processes in our body after consuming lunch meals. It being a widely known concept, there are ample studies that focus on its effects on academic performance of students. While this dip in energy does affect student engagement to a large extent, there are also studies on how the teaching-learning methods are a factor affecting academic performance. However, there are no relevant studies that aim to look at both these factors in tandem, i.e. the effect of lunch along with the teaching-learning methods in the afternoon classrooms as factors influencing academic performance and student engagement. This paper aims to study this area of topic hypothesising that having a more balanced lunch along with engaging in more interactive classes would positively correlate with student engagement in afternoon classes. The study was conducted with bachelors and masters students at the CRI, University of Paris through surveys that were sent to students at the end of their class day. </p> <p>Due to lack of adequate data, the study was not able to produce significant correlations. However smaller nuances of the relationship between lunch, teaching-learning methods and student engagement were found. While engagement in classrooms did seem to be maximum when it was interactive and only 50% of students' lunch was formed of carbohydrates, no significant correlations were found to confirm the hypotheses.</p>
VirHunter: a deep learning-based method for detection of novel RNA viruses in plant sequencing data
<p>This storage contains 2 archives: toy datasets to test the training of the VirHunter and weights of the fully trained VirHunter models for 3 host species (peach, grapevine, sugar beet) and for fragment sizes 500 and 1000. .</p> <p>The toy dataset consists of 3 archived files: 'viruses.fasta', 'host.fasta', 'bacteria.fasta'.</p> <p>'viruses.fasta' contains 10000 randomly selected plant viruses from the virus dataset described in the paper.</p> <p>'host.fasta' consists of peach chromosome 2.</p> <p>'bacteria.fasta' consists of 10 bacterial genomes selected randomly: GCF_000284415, GCF_000590555, GCF_001548055, GCF_002795265, GCF_003330825, GCF_003957805, GCF_005845345, GCF_009176625, GCF_010748935, GCF_014681765</p> <p> </p>
A consistent discretization of the single-field two-phase momentum convection term for the unstructured finite volume Level Set / Front Tracking method - data
<p>Research data from the rhoLENT unstructured Level Set / Front Tracking method for simulating two-phase flows with large density ratios. </p>
Efficient embryoid-based method to improve generation of optic vesicles from human induced pluripotent stem cells data
<p>Animal models have provided many insights into ocular development and disease, but they remain suboptimal for understanding human oculogenesis. Eye development requires spatiotemporal gene expression patterns and disease phenotypes can differ significantly between humans and animal models, with patient-associated mutations causing embryonic lethality reported in some animal models. The emergence of human induced pluripotent stem cell (hiPSC) technology has provided a new resource for dissecting the complex nature of early eye morphogenesis through the generation of three-dimensional (3D) cellular models. By using patient-specific hiPSCs to generate <em>in vitro </em>optic vesicle-like models, we can enhance the understanding of early developmental eye disorders and provide a pre-clinical platform for disease modelling and therapeutics testing. A major challenge of <em>in vitro </em>optic vesicle generation is the low efficiency of differentiation in 3D cultures. To address this, we adapted a previously published protocol of retinal organoid differentiation to improve embryoid body formation using a microwell plate. Established morphology, upregulated transcript levels of known early eye-field transcription factors and protein expression of standard retinal progenitor markers confirmed the optic vesicle/presumptive optic cup identity of <em>in vitro </em>models between day 20 and 50 of culture. This adapted protocol is relevant to researchers seeking a physiologically relevant model of early human ocular development and disease with a view to replacing animal models.</p>
A refined method for studying foraging behaviour and body mass in group-housed European starlings.
<p>Datasets and R script corresponding to the following manuscript:</p> <p>A refined method for studying foraging behaviour and body mass in group-housed European starlings.</p> <p>Laboratory experiments on passerine birds have been important for testing hypotheses regarding the effects of environmental variables on the adaptive regulation of body mass. However, previous work in this area has suffered from poor ecological validity and animal welfare due to the requirement to house birds individually in small cages to facilitate behavioural measurement and frequent catching for weighing. Here we describe the social foraging system, a novel technology that permits continuous collection of individual-level data on operant foraging behaviour and body mass from group-housed European starlings (<em>Sturnus vulgaris</em>). We demonstrate rapid acquisition of operant key pecking, followed by foraging and body mass data from two groups of six birds maintained on a fixed-ratio operant schedule under closed economy for 11 consecutive days. Birds gained 6.0 ± 1.2 g (mean ± sd) between dawn and dusk each day and lost an equal amount overnight. Individual daily mass gain trajectories were non-linear, with the rate of gain decelerating between dawn and dusk. Within-bird variation in daily foraging effort (key pecks) positively predicted within-bird variation in dusk mass. However, between-bird variation in mean foraging effort was uncorrelated with between-bird variation in mean mass, potentially indicative of individual differences in daily energy requirements. We conclude that the social foraging system delivers refined data collection and offers potential for improving our understanding of mass regulation in starlings and other species.<strong> </strong></p>
Crop classification dataset for testing domain adaptation or distributional shift methods
<p>In this upload we share processed crop type datasets from both France and Kenya. These datasets can be helpful for testing and comparing various domain adaptation methods. The datasets are processed, used, and described in this paper: <a href="https://doi.org/10.1016/j.rse.2021.112488">https://doi.org/10.1016/j.rse.2021.112488</a> (arXiv version: <a href="https://arxiv.org/pdf/2109.01246.pdf">https://arxiv.org/pdf/2109.01246.pdf</a>). </p> <p>In summary, each point in the uploaded datasets corresponds to a particular location. The label is the crop type grown at that location in 2017. The 70 processed features are based on Sentinel-2 satellite measurements at that location in 2017. The points in the France dataset come from 11 different departments (regions) in Occitanie, France, and the points in the Kenya dataset come from 3 different regions in Western Province, Kenya. Within each dataset there are notable shifts in the distribution of the labels and in the distribution of the features between regions. Therefore, these datasets can be helpful for testing for testing and comparing methods that are designed to address such distributional shifts.</p> <p>More details on the dataset and processing steps can be found in <a href="https://doi.org/10.1016/j.rse.2021.112488">Kluger et. al. (2021)</a>. Much of the processing steps were taken to deal with Sentinel-2 measurements that were corrupted by cloud cover. For users interested in the raw multi-spectral time series data and dealing with cloud cover issues on their own (rather than using the 70 processed features provided here), the raw dataset from Kenya can be found in <a href="https://openreview.net/forum?id=5HR3vCylqD">Yeh et. al. (2021)</a>, and the raw dataset from France can be made available upon request from the authors of this Zenodo upload.</p> <p>All of the data uploaded here can be found in "CropTypeDatasetProcessed.RData". We also post the dataframes and tables within that .RData file as separate .csv files for users who do not have R. The contents of each R object (or .csv file) is described in the file "Metadata.rtf".</p> <p><strong>Preferred Citation:</strong></p> <p>-Kluger, D.M., Wang, S., Lobell, D.B., 2021. Two shifts for crop mapping: Leveraging aggregate crop statistics to improve satellite-based maps in new regions. Remote Sens. Environ. 262, 112488. https://doi.org/10.1016/j.rse.2021.112488.</p> <p>-URL to this Zenodo post https://zenodo.org/record/6376160</p>
DIRECTLib - a library of global optimization problems for DIRECT-type methods
<p><strong>DIRECTLib - a library of a box and generally-constrained global optimization problems for DIRECT-type methods</strong></p> <p>In this library, we present an extended collection of a box and generally constrained global optimization test problems (in MATLAB format) typically used in benchmarking various DIRECT-type [1] methods in the relevant literature (see, e.g., [2-6] and references given therein).</p> <p>File: <strong>WCGO_Test_results.xlsx </strong>contains<strong> </strong>experimental results presented in: <a href="https://arxiv.org/abs/2109.14912">https://arxiv.org/abs/2109.14912</a></p> <p><strong>References</strong></p> <ol> <li>Jones, D. R., Perttunen, C. D. and Stuckman, B. E. (1993) ‘Lipschitzian optimization without the Lipschitz constant’, <em>Journal of Optimization Theory and Applications</em>, 79(1), pp. 157–181. <strong>doi</strong><strong>: 10.1007/BF00941892</strong>.</li> <li> <p>R. Paulavičius, J. Žilinskas. (2014) Simplicial Global Optimization, SpringerBriefs in Optimization, Springer New York, New York, NY. <strong>doi:10.1007/978-1-4614-9093-7</strong></p> </li> <li> <p>L. Stripinis, R. Paulavičius, J. Žilinskas. (2018) Improved scheme for selection of potentially optimal hyper-rectangles in DIRECT, Optimization Letters 12 (7) 1699–1712. <strong>doi:10.1007/s11590-017-1228-4</strong></p> </li> <li> <p>L. Stripinis, R. Paulavičius, J. Žilinskas. (2019) Penalty functions and two-step selection procedure based DIRECT-type algorithm for constrained global optimization, Structural and Multidisciplinary Optimization 59 (6) 2155–2175. <strong>doi:10.1007/s00158-018-2181-2</strong>.</p> </li> <li> <p>L. Stripinis, J. Žilinskas, L. G. Casado, R. Paulavičius (2021) On MATLAB experience in accelerating DIRECT-GLce algorithm for constrained global optimization through dynamic data structures and parallelization. <em>Applied Mathematics and Computation</em>, <a href="https://doi.org/10.1016/j.amc.2020.125596">DOI: 10.1016/j.amc.2020.125596</a></p> </li> <li> <p>L. Stripinis, R. Paulavičius (2021) A new DIRECT-GLh algorithm for global optimization with hidden constraints. <em>Optimization Letters</em>, 15, p. 1865-1884, <a href="https://doi.org/10.1007/s11590-021-01726-z">DOI: 10.1007/s11590-021-01726-z</a></p> </li> </ol>
Towards a systematic approach to manual annotation of code smells - C# Dataset of Long Method and Large Class code smells
<p>This dataset includes open-source projects written in C# programing language, annotated for the presence of Long Method and God Class code smells. Each instance was manually annotated by at least two annotators. We explain our motivation and methodology for creating this dataset in our <a href="https://www.techrxiv.org/articles/preprint/Towards_a_systematic_approach_to_manual_annotation_of_code_smells/14159183/1">preprint</a>:</p> <p>Luburić, N., Prokić, S., Grujić, K.G., Slivka, J., Kovačević, A., Sladić, G. and Vidaković, D., 2021. Towards a systematic approach to manual annotation of code smells. </p> <p>The dataset contains two excel datasheets:</p> <ul> <li><em>DataSet_Large Class.xlsx</em> – C# classes annotated for the Large Class code smell severity.</li> <li><em>DataSet_Long Method.xlsx</em> – C# methods annotated for the Long method code smell severity.</li> </ul> <p> The columns in the datasheet represent:</p> <ul> <li><em>Code Snippet ID</em> – the full name of the code snippet. <ul> <li>For classes, this is the package/namespace name followed by the class name. The full name of inner classes also contains the names of any outer classes (e.g., <em>namespace.subnamespace.outerclass.innerclass</em>).</li> <li>For methods, this is the full name of the class and the methods’s signature (e.g., <em>namespace.class.method(param1Type, param2Type)</em> ).</li> </ul> </li> <li><em>Link </em>– The GitHub link to the code snippet, including the commit and the start and end LOC.</li> <li><em>Code Smell </em>– code smell for which the code snippet is examined (Large Class or Long Method).</li> <li><em>Project Link </em>– the link to the version of the code repository that was annotated.</li> <li><em>Metrics </em>– a list of metrics for the code snippet, calculated by our <a href="https://github.com/Clean-CaDET/platform#readme">platform</a>. Our dataset provides 25 class-level metrics for Large Class detection and 18 method-level metrics for Long Method detection The list of metrics and their definitions is available <a href="https://github.com/Clean-CaDET/platform/blob/c4acff95ec00ff6c25fa62dde4818c1f40e39d39/CodeModel/CaDETModel/CodeItems/CaDETMetrics.cs">here</a>.</li> <li><em>Final annotation </em>– a single severity score calculated by a majority vote. </li> <li><em>Annotators </em>– each annotator's (1, 2, or 3) assigned severity score.</li> </ul> <p>To help guide their reasoning for evaluating the presence and the severity of a code smell, three annotators independently annotated whether the considered heuristics apply to an evaluated code snippet. We provide these results in two separate excel datasheets:</p> <ul> <li><em>LargeClass_Heuristics.xlsx </em>- C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> <li><em>LongMethod_Heuristics.xlsx </em>- C# classes annotated for the presence of heuristics relevant for the Large Class code smell.</li> </ul> <p>The columns of these two datasheets are:</p> <ul> <li><em>Code Snippet ID </em>- the full name of the code snippet (matching the IDs from <em>DataSet_Large Class.xlsx </em>and <em>DataSet_Long Method.xlsx</em>)</li> <li><em>Annotators</em> – heuristics labelled by each of the annotators (1, 2, or 3).</li> <li><em>Heuristics </em>– whether the heuristic is applicable to the examined code snippet or not (Section 1.2.4 lists heuristics relevant for the Large Class detection, and Section 1.2.5 lists the heuristics relevant for the Long Method detection).</li> </ul>
Butcher's tableaux of the optimized explicit Runge-Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics
<p>This folder contains the Butcher's tableaux of the optimized explicit Runge-Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics presented in Al Jahdali et al., "Optimized explicit Runge--Kutta schemes for high-order collocated discontinuous Galerkin methods for compressible fluid dynamics," Computers & Mathematics with Applications, 2022.</p> <p>Specifically,</p> <p><a href="https://zenodo.org/api/files/754318a5-0881-4252-9059-086da4607b49/Butcher_coefficients_ADV.txt">Butcher_coefficients_ADV.txt</a> contains the Butcher's tableaux of the explicit Runge-Kutta schemes optimized using the spectra of the 2D advection equation.</p> <p><a href="https://zenodo.org/api/files/754318a5-0881-4252-9059-086da4607b49/Butcher_coefficients_IEV.txt">Butcher_coefficients_IEV.txt</a> contains the Butcher's tableaux of the explicit Runge-Kutta schemes optimized using the spectra of the isentropic vortex propagation for the compressible Euler equations.</p> <p> </p> <p> </p> <p> </p>
EPTGODD-WHU: Ensemble Precipitation and Temperature from CMIP6 GCMs optimized by OLS-DT-DNN methods integration (1850-2100)
<p>This monthly global climate dataset EPTGODD-WHU (precipitation and mean temperature variables with grid size of 0.5°×0.5°) was ensembled from 16 selected CMIP6 GCMs. The published dataset was optimized by OLS (Ordinary Linear Square)-DT (Decision Tree)-DNN (Deep Neural Network) methods integration. The CF (Climate and Forecast) v1.6 was employed as the guideline for NetCDF4 format. The periods of temperature files can be divided into historical (1850-1900) and future (2015-2100) periods. For precipitation, this product provides future (2015-2100) period. Three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) were selected for both variables. The units of this dataset are degrees Celsius and mm/month for temperature and precipitation, respectively. Each NetCDF4 file in this dataset includes three dimensions (time, latitude (-89.75°N to 89.75°N) and longitude (-179.75°E to 179.75°E)).</p>
WaterGAP2.2d model derived Potential evapotranspiration and Renewable water resources variables with standard and modified PET calculation methods
<p>This data set is produced as a part of the ''Improving the quantification of climate change hazards by hydrological models: A simple ensemble approach for considering the uncertain effect of vegetation response to climate change on potential evapotranspiration" journal publication (in preparation). WaterGAP2.2d global hydrological model with two different settings; 1) with standard PET method Priestley-Taylor (PT) and 2) with modified approach (PT-MA) (please refer to the publication for more details on the method) used to derive the data set. The bias-adjusted GCM-derived (GFDL-ESM2M, HadGEM2-ES, IPSL-CM5A-LR, and MIROC5) climate data under RCP2.6 and RCP8.5 emission scenarios were used as the input. The model-derived potential evapotranspiration and the renewable water resources variables are available from 1981 to 2099 on the monthly scale for each land grid cell (spatial resolution: 0.5 degrees x 0.5 degrees). The data files are in the netCDF format (.nc4). </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.