Find research datasets worth reusing

opencc-by-4.0Aug 2024View details →

Interactive Outputs for "Automated Mixture Analysis via Structural Evaluation"

HTML files with interactive outputs related to the mixture assignments discussed in the paper "Automated Mixture Analysis via Structural Evaluation."

opencc-by-4.0Aug 2024View details →

Dataset for evaluating rooftop photovoltaic solar panels impact on urban temperature at city scale

This dataset contains simulation outputs from the WRF/BEP+BEM v4.3.3 model, evaluating the impact of rooftop photovoltaic solar panels on urban temperatures in five cities: Kolkata, Austin, Sydney, Athens, and Brussels. The model uses one parent domain and two nested domains with resolutions of 18 km, 6 km, and 2 km, focusing on the 2 km resolution. Simulations cover the summer periods of each city, including  panel surface temperature and urban state variables data. The dataset represents diverse climate types: tropical wet and dry (Kolkata), Mediterranean (Athens), moderate oceanic (Brussels), and subtropical humid (Sydney and Austin).

opencc-by-4.0Jun 2024View details →

Hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity

Entitled “A novel hybrid deep learning framework for evaluating field evapotranspiration considering the impact of soil salinity” for possible publication in Water Resources Research. Data: The Salinized Farmland Flux Station sites are located in the typical irrigated agricultural area of the arid continental monsoon region in northwest China.In this study, we used data from four flux tower located in saline farmland: two for maize (MZ1, MZ2) and two for sunflower (SF1, SF2). For each site, we collected the following variables at half-hourly temporal resolution: (i) latent heat (LE, W m-2) fluxes, serving as a direct measure representing the energy component of ET (mm h-1), (ii) net radiation (Rn, W m-2), (iii) ground heat flux (G, W m-2), (iv) solar irradiance (Rs, W m-2), (v) air temperature (Ta, °C), (vi) vapor pressure deficit (VPD, KPa), (vii) wind speed (Us, m s-1), (viii) relative humidity (RH, %), and (ix) atmospheric carbon dioxide concentration (Ca, mg m-3). During the crop growth period, field in-situ measurements of soil and vegetation data from these farmlands are conducted approximately every 10 days.   Code: All the codes were executed in Python. The provided hybrid deep learning model code can be run on Jupyter Notebook.

Evaluating Grid Affordability Across Multi-Tier Framework in 48 Sub-Saharan African Countries

This folder supports a master's thesis titled 'Bridging the Energy Divide: Evaluating Grid Affordability and Expansion Feasibility Using a Multi-Tier Framework Across 48 Sub-Saharan African Countries.' It includes all relevant documents, input data, and code for analysing grid electricity affordability in these 48 SSA countries.

Data for 'Evaluating the role of enhanced weathering in marine carbon dioxide removal under high emission pathway'

1. ALK.mat <a name="OLE_LINK86"></a><a name="OLE_LINK87"></a>Description: This file includes three structs, ALK_CTL, ALK_OWE, ALK_NUT, representing alkalinity data under control run, OWE simulation, NUT simulation. Each struct includes 5 parameters, lat: latitude, lon: longitude, mean_sur: variation of average alkalinity for the upper 100 m, mean_total: variation of average alkalinity for the whole water column, para_a10: 10-years average alkalinity for the upper 100 m.  Units: meq/m3 Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig. 1a, b and Fig.2   2. pH.mat Description: This file includes three structs, pH_CTL, pH_OWE, pH_NUT, representing surface pH data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pH, para_a10: 10-years average of surface pH.  Units: unitless <a name="OLE_LINK92"></a><a name="OLE_LINK93"></a>Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig. 1c and Fig.3   3. total_DIC_ALK.mat <a name="OLE_LINK88"></a><a name="OLE_LINK89"></a>Description: This file includes six structs, CTLALKt, CTLDICt, OWEALKt, OWEDICt, NUTALKt, NUTDICt, representing integrated alkalinity and DIC data in control run, OWE simulation, NUT simulation. Each struct includes two parameters, para_a: decadal average of DIC inventory, para_int: the sum of global DIC inventory. Units: mmol (DIC)/ meq (ALK) (we plot the figure with the unit Tmol in Fig. 4 and Pmol in Fig. 1d) Data in this file is used in Fig. 1d and Fig. 4.   4. remapped_DIC_CTL.nc, remapped_DIC_OWE.nc and DIC_remapped_NUT.nc Description: The three .nc files include remapped standard-grid (360*180) DIC inventory under control run, OWE simulation and NUT simulation. Each .nc file has 4 variables, lon: longitude, lat: latitude, z_t: depth, DIC: remapped DIC.  Unit: mmol/m3 Coordinates: DIC: longitude * latitude* depth* time Data in this file is used in Fig. 5   5. pCO2.mat Description: This file includes three structs, pCO2_CTL, pCO2_OWE, pCO2_NUT, representing surface pCO2 data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of surface pCO2, para_a10: 10-years average of surface pCO2. Unit: ppmv Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig.6   6. FG_CO2.mat <a name="OLE_LINK94"></a><a name="OLE_LINK95"></a>Description: This file includes three structs, <a name="OLE_LINK90"></a><a name="OLE_LINK91"></a>FG_CO2_CTL, FG_CO2_OWE, FG_CO2_NUT, representing DIC surface gas flux data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of FG_CO2, para_a10: 10-years average of FG_CO2. Unit: mmol/m3 cm/s (Need to convert unit into mol/m2/yr) <a name="OLE_LINK96"></a><a name="OLE_LINK97"></a>Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig. 7   7. NPP.mat <a name="OLE_LINK98"></a><a name="OLE_LINK99"></a>Description: This file includes three structs, NPP_CTL, NPP_OWE, NPP_NUT, representing total C fixation vertical integral data in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of NPP, para_a10: 10-years average of NPP. Unit: mmol/m3 cm/s (Need to convert unit into mol/m2/yr) <a name="OLE_LINK104"></a><a name="OLE_LINK105"></a>Coordinates: Para_a10: longitude * latitude * time Data in this file is used in Fig. 8   8. spC.mat, diatC.mat, diazC.mat Description: The three files include phytoplankton biomass of small phytoplankton (spC_CTL, spC_OWE, spC_NUT), diatom (<a name="OLE_LINK100"></a><a name="OLE_LINK101"></a>diatC_CTL, diatC_OWE, diatC_NUT), and diazotroph (diazC_CTL, diazC_OWE, diazC_NUT). All phytoplankton biomass is measured in the unit of carbon. We store data on each phytoplankton species in the form of a struct. Each struct has five parameters, lat: latitude, lon: longitude,  mean_sur: variation of <a name="OLE_LINK102"></a><a name="OLE_LINK103"></a>average of upper 100 m phytoplankton biomass; mean_total: variation of average phytoplankton biomass in the upper ocean, para_a10: 10-years average of average phytoplankton biomass in the upper ocean.  Unit: mmol/m3 <a name="OLE_LINK108"></a><a name="OLE_LINK109"></a>Coordinates: Para_a10: longitude * latitude * time <a name="OLE_LINK110"></a><a name="OLE_LINK111"></a>Data in these files are used in Fig. 9   9. <a name="OLE_LINK106"></a><a name="OLE_LINK107"></a>calcToSed.mat Description: This file includes three structs, calcToSed_CTL, calcToSed_OWE, calcToSed_NUT, representing CaCO3 flux to sediments flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of CaCO3 flux,  para_a10: 10-years average of CaCO3 flux.  Unit: nmol/cm2/s (need to convert unit to mol/m2/yr) Coordinates: Para_a10: longitude * latitude * time <a name="OLE_LINK112"></a><a name="OLE_LINK113"></a>Data in these files are used in Fig. 10   10. POC.mat Description: This file includes three structs, POC_CTL, POC_OWE, POC_NUT, representing 100 m POC flux in control run, OWE simulation, NUT simulation. Each struct includes 4 parameters, lat: latitude, lon: longitude, mean_sur: variation of flux, para_a10: 10-years average of flux. Unit: mmol/m3 cm/s (Need to convert unit into mol/m2/yr) Coordinates: Para_a10: longitude * latitude * time Data in these files are used in Fig. 11   11. atmoCO2.mat Description: This file includes three structs, CTLATM, OWEATM, NUTATM. In each struct, atm_co2_trend represent the atmospheric CO2 variation with the unit ppm. Data in this file is used in Fig.1f

Evaluation Results

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Evaluating Machine Learning Models for Supernova Gravitational Wave Signal Classification

This dataset contains gravitational wave (GW) data used in our research work <a href="https://doi.org/10.1088/2632-2153/ada33a" target="_blank" rel="noopener">Abylkairov et al. (2024)</a>. The first 10,000 columns represent the gravitational wave strain D · h [cm] for the corresponding time values ranging from -993 ms to 6.9 ms, with a step size of 0.1 ms. The zero time refers to the time of core bounce. Each row within these first 10,000 columns corresponds to 864 different gravitational wave signals. Columns 10,001 to 10,005 contain the following additional parameters: <ul> <li>T/|W|: The rotational parameter.</li> <li>GR_or_GREP: Binary indicator for the signal type, where 0 denotes GR and 1 denotes GREP.</li> <li>EOS: The equation of state (EOS) model, where 0 corresponds to SFHo, 1 to LS220, 2 to HSDD2, and 3 to GShenFSU2.1.</li> <li>f_peak: The peak frequency [Hz].</li> <li>D Delta h: D · ∆h [cm].</li> </ul> For each row (representing a single gravitational wave signal), these parameters provide information about the signal's rotational parameter, type (GR or GREP), EOS model, peak frequency, and D · ∆h. Note: In the f_peak calculation procedure, we truncated the GW signal at 4.5 ms after the end of the core bounce (see <a href="https://doi.org/10.1103/PhysRevD.95.063019">Richers et al. (2017)</a> for details).

Out-of-Distribution Evaluation of Active Learning Pipelines for Molecular Property Prediction Dataset

Data for the paper "Out-of-Distribution Evaluation of Active Learning Pipelines for Molecular Property Prediction"

CAABA/MECCA model output for evaluating optimized step size control in Rosenbrock solvers for stiff ODEs

This dataset comprehend the simulation output obtained with the CAABA/MECCA model for the optimization of the step size control in the Rosenbrock integrators available from the Kinetic PreProcessor (KPP) version 2.2.3_rs4. This dataset is made avalaible for the peer-review of the manuscript describing the model and the different options of step size control that was implemented.

Datasets corresponding to "Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets"

Datasets corresponding to "Direct evaluation of antiplatelet therapy in coronary artery disease by comprehensive image-based profiling of circulating platelets" 02_CNN_PhenotypeClassif.7z CNN Phenotype classification. Model was trained using AIDeveloper. using manually labelled data. Labelled Data is contained in folder "03_GatedData". The AIDeveloper session file in "02_Model\M10_Nitta6l_32pix_8class_meta.xlsx" shows, which files correspond to which subpopulation. The final model "M10_Nitta6l_32pix_8class_448.model" and corresponding .pb files are also located in that folder. codeforclassification.zip Code for applying model on unlabelled data. Test data is contained in 'sampledata.zip'

opencc-by-4.0Jun 2024View details →

Towards Musically Informed Evaluation of Piano Transcription Models

We provide here the evaluation set employed in our experiments described in "Towards Musically Informed Evaluation of Piano Transcription Models", published in the Proceedings of the 25th International Society for Music Information Retrieval Conference (ISMIR), San Francisco, United States, 2024. In this work, we demonstrate musically informed piano transcription metrics using transcriptions derived from three state-of-the-art transcriptions ([1], [2], [3]). To this end, we create an evaluation set that includes (1) a subset of the original audio recordings from the MAESTRO dataset [1], (2) a re-recorded version that subset, and (3) a perturbed version of recordings from both (1) and (2). In this data repository, we provide components (2) and (3). [1] 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.   [2] Qiuqiang Kong, Bochen Li, Xuchen Song, Yuan Wan, and Yuxan Wang, “High-resolution piano transcription with pedals by regressing onset and offset times,” IEEE/ACM Transactions on Audio, Speech and Language Processing, vol. 29, pp. 3707–3717, 2021.   [3] Curtis Hawthorne, Ian Simon, Rigel Swavely, Ethan Manilow, and Jesse Engel. “Sequence-to-sequence piano transcription with transformers,” in Proceedings of the 22nd International Society for Music Information Retrieval Conference, ISMIR 2021.

Evaluated decay data of Sm-151

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Evaluating the Impact of Triple Compound Therapy on Relapse Frequency and Quality of Life in Children with Severe Atopic Dermatitis: A Phase II Randomised Clinical Trial

Datasets and analysis from A multi-centre, randomized controlled trial, on pediatric Atopic Dermatitis, conducted at Red Cross and Walter Sisulu Hospitals. The study is entitled Evaluating the Impact of Triple Compound Therapy on Relapse Frequency and Quality of Life in Children with Severe Atopic Dermatitis: A Phase II Randomised Clinical Trial.

Supplementary Table S1. Combined analysis of variance containing the degrees of freedom (DF), mean squares (MS), P value (P val.), mean, coefficient of experimental variation (CEV%) and selective accuracy (SA) for the traits of luminosity (L), chromaticity a (a), chromaticity b (b*), grain length (length, mm), grain width (width, mm), grain thickness (thickness, mm), mass of 100 grains (Mass, g), normal grains (Ng, %), water absorption (absorption, %), cooking time (Ct, min:s), and concentrations of potassium (K, g kg-1 dry matter - DM), phosphorus (P, g kg-1 DM), calcium (Ca, g kg-1 DM), magnesium (Mg, g kg-1 DM), iron (Fe, mg kg-1 DM), zinc (Zn, mg kg-1 DM), and copper (Cu, mg kg-1 DM) obtained in 25 common bean cultivars evaluated in four experiments carried out from 2019 to 2021

Table S1. Combined analysis of variance. Indirect selection for multiple technological and nutritional traits in common bean cultivars under different degrees of multicollinearity Bragantia, 2024.

opencc-by-4.0Apr 2024View details →

Simulated EEG and EMG data for Reference Phase Analysis evaluation.

This dataset comprises simulated EEG (Electroencephalography) signals recorded from 29 channels along with an EMG (Electromyography) signal. The signals have been artificially generated using real EEG and EMG signals as the basis. Five distinct source models were generated, each comprising a different number of sources. We created models with 2, 3, 4, 5, and 6 sources. Furthermore, we add additive noise to the signals encompassing SNRs ranging from 90 to 0 dB and a phase jitter ranging from 0.1 to 1.5 radians. We generated EEG and EMG signals using MATLAB and employed a forward model, created with FieldTrip, to convert signals from the source space to the electrode space. EEG measures were computed utilizing a linear mixture model. The simulated dataset aims to mimic the characteristics of real EEG and EMG signals, including their temporal dynamics, noise, and frequency spectra. It serves as a valuable resource for developing and testing signal processing algorithms, and brain-computer interfaces in the fields of neuroscience and biomedical engineering.

Transgender Stigma - Evaluation of Online Course

The course was offered free of charge on Coursera and contained 19 teaching videos (3-7 minutes each), intermittent practice quizzes, and discussion prompts. Employing real voice recordings of transgender children and their caregivers, the videos were designed to elicit empathy and transmit knowledge. Most videos were animated and used a narrative format. 447 participants distributed around the globe completed a survey both prior to and after finishing the course. The survey contained five questions that captured subjects’ levels of transgender stigma. Results of the pre- and post-surveys were then compared. Location and identification number are removed.

CRITER 1.0: Sea Surface Temperature Evaluation Datasets

Training and evaluation datasets used in CRITER 1.0: A coarse reconstruction with iterative refinement network for sparse spatio-temporal satellite data .

Data from 'Sustainability in the laboratory: evaluating the reuseability of microtiter plates for PCR and fragment detection'

This repository contains the data for the manuscript: Sustainability in the laboratory: evaluating the reuseability of microtiter plates for PCR and fragment detection. The script can be found here: https://github.com/AneLivB/SGP Files:  R15*_R*.csv The processed data used for the script. All other files of the same name format contain the same elements! <ul> <li> Columns: Rack_location, ID, all loci <ul> <li> Rack_location: the location on the microwell plate the individual had in the wet lab </li> <li> ID: individual identification tag of the animal the DNA sample stems from </li> <li> _a and _b denotes the first and second allele of all loci </li> </ul> </li> </ul> Mismatches A dataframe created by the script and later used to calcualte per treatment single-locus genotype error rates. <ul> <li> Columns: Rack.1, Rack.2, Treatment, No..of.mistyped.alleles, No..of.mistyped.reactions, No..of.reactions, Allelic.error.rate, Genotype.error.rate <ul> <li> Rack.1: One of three racks (R154, R155, R156) from the standard protocol </li> <li> Rack.2: One of three racks (R154, R155, R156) from one of the other treatments (e.g. R154_R2, R154_R3, R154_R4) </li> <li> Treatment: One of three treatments (Internal control, Reused detection plate, Reused PCR plate) </li> <li> No..of.mistyped.alleles: Number of mismatched alleles within one treatment group, within a DNA plate </li> <li> No..of.mistyped.reactions: Number of mismatched single-locus genotypes within one treatment group, within a DNA plate </li> <li> No..of.reactions: Number of single-locus genotypes within one treatment group, within a DNA plate </li> <li> Allelic.error.rate: Error rate per allele </li> <li> Genotype.error.rate: Error rate per single-locus genotypes </li> </ul> </li> </ul> model.mismatch.2.Rdata RData file containing the model output.   Manuscript abstract:  Single-use plastics (SUPs) are indispensable in laboratory research, but their disposal contributes substantially to environmental pollution. Consequently, reusing common SUP items such as microtiter plates represents a promising strategy for improving laboratory sustainability. However, the key challenge lies in determining whether SUP reuse can be implemented without sacrificing data quality. To investigate this, we conducted a simple experiment to assess the impact of reusing microtiter plates on microsatellite genotyping accuracy. Plates previously used for PCR and fragment detection were cleaned using an environmentally friendly method and then reused. Our results indicate that, while reusing PCR plates significantly increases genotyping error rates due to residual DNA contamination, detection plates can be reused without compromising data quality. Our approach offers laboratories a practical and sustainable option for reducing SUP waste and costs while maintaining research integrity.

Evaluating rapid water excitation techniques for 5D whole-heart fat-suppressed free-running cardiac MRI at 1.5T scanner

Volunteer dataset of the paper: "Evaluating Rapid Water Excitation Techniques for 5D Whole-Heart Fat-Suppressed Free-Running Cardiac MRI at 1.5T Scanner" The folder "InVivo" contains raw data from five different volunteers, with each volunteer having undergone 5 different acquisitions: bSSFP, FISS, BORR, LIBRE, and LIBOR.