Find research datasets worth reusing

NGII Data Set for Black Ice Traffic Accident Prediction

<a href="../api/records/10863284/draft/files/NGII%20Data%20Set%20for%20Black%20Ice%20Traffic%20Accident%20Prediction.zip/content" target="_blank" rel="noopener noreferrer">Title: NGII Data Set for Black Ice Traffic Accident Prediction</a> This dataset has been processed for scholarly purposes, utilizing data provided by the National Geographic Information Institute of Korea. <Reference> National Geographic Information Institute. (n.d.). National Land Information Platform. Retrieved from https://map.ngii.go.kr/ms/map/NlipMap.do

Comparative membrane proteomic analysis of Tritrichomonas foetus isolates (non filtered Data Sets)

Tritrichomonas foetus is a flagellated and anaerobic parasite able to infect cattle and felines. Despite its prevalence, there is no effective standardized or legal treatment for T. foetus-infected cattle; the vaccination still has limited success in mitigating infections and reducing abortion risk; and nowadays, the diagnosis of T. foetus presents important limitations in terms of sensitivity and specificity in bovines. Here, we characterize the plasma membrane proteome of T. foetus and identify proteins that are represented in different isolates of this protozoan.  Raw proteomics data sets from MALDI-TOF Mass Spectrometry presented here corresponds to six T. foetus isolates (Tf0-Tf5). For Tf2 isolate also five membrane fractions are presented (f1-f5).

opencc-by-4.0Feb 2024View details →

Capturing Periodic I/O Using Frequency Techniques [Data Set]

<div>This file contains the data set from the paper: "Capturing Periodic I/O Using Frequency Techniques," which was accepted at the IPDPS 2024. <div>The Instructions are provided in the <a href="https://github.com/tuda-parallel/FTIO/tree/main/artifacts/ipdps24">FTIO GitHub</a>: https://github.com/tuda-parallel/FTIO/tree/main/artifacts/ipdps24</div> </div> <div> </div> <div>After extracting data.zip, the folder named data has the following structure:</div> <div> <pre>data ├── application_traces │   ├── HACC-IO │   ├── IOR │   ├── LAMMPS │   ├── NEK5000 │   └── README.md ├── exps_with_synthetic_traces ├── iosets_ftio_experiments └── README.md</pre> </div> <div> The folder iosets_ftio_experiments and exps_with_synthetic_traces in data.zip are snapshots from the repositories: <ul> <li><a href="https://gitlab.inria.fr/hpc_io/iosets-ftio-experiments">https://gitlab.inria.fr/hpc_io/iosets-ftio-experiments</a></li> <li><a href="https://gitlab.inria.fr/hpc_io/ftio_paper_exps_with_synthetic_traces">https://gitlab.inria.fr/hpc_io/ftio_paper_exps_with_synthetic_traces</a></li> </ul> </div>

NemNet try out data set

This data set contains publicly available monitoring data of NEMNET <a href="https://www.onderzoeksfaciliteiten.nl/node/3910">National Environmental Monitoring Network | Grootschalige wetenschappelijke infrastructuur (onderzoeksfaciliteiten.nl)</a>

Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS) - code and data sets

This repository contains the scripts for the paper in revision to the AAPS J: Prediction of individual disease progression including parameter uncertainty in rare neurodegenerative diseases: the example of Autosomal-Recessive Spastic Ataxia Charlevoix Saguenay (ARSACS)  Authors: Niels Hendrickx, MSc, France Mentré, MD, PhD, Andreas Traschütz, MD, PhD, Cynthia Gagnon, PhD, Rebecca Schüle, MD, ARCA Study Group, EVIDENCE-RND consortium, Matthis Synofzik, MD, Emmanuelle Comets, PhD A simulated dataset (simulated_arsacs.csv) has been included in the repository to make the code executable as a standalone. Four main scripts have been provided in addition with the present Readme describing the files. The repository also includes 3 R objects and 2 folders which will be overwritten when the scripts are run, and are included as examples of the expected outputs. The main scripts are: - Script_imputation_selection.R: runs the covariate selection method. It uses a simulated dataset provided in the depot. The multiple imputation model is hardcoded as an input to the mice package to generate 10 imputed datasets, saved in current_directory/imputed_data_sets/df_arsacs_mi_i.csv. The script then runs the covariate selection method. The script prints out the list of selected covariates and returns a saemixObject containing the fit of the selected covariate model.  After the script executes, a list will be saved with the name of the selected covariates in the current directory (an example is included under the name "cov_matrix_model.RData" in the repository), the output of the selection, containing the whole history of runs will be saved under "final_covariate_model.RData", the list of selected covariate names will be saved under "list_covariates.RData". - source_mi.R: contains the functions used by Script_imputation_selection.R - script_bootstrap_indfit.R: This script loads "cov_matrix_model.RData" containing the matrix of covariate effects (used by saemix) and "list_covariates.RData", the list of covariates included, fits the model on the imputed data sets and computes its bootstrap distribution for each imputed data set (in the script, using only 20 samples for computation time, saved in current_directory/bootstrap/boot.arsacs.case.mi.i). It then computes the mean parameter and relative standard error of each parameter. It then computes the conditional distribution of each patient in each bootstrap samples and returns a data frame of individual predictions. The script will then plot 4 indivudal predictions.  - source_bootstrap.R: contains the functions used by script_bootstrap_indfit.R Both scripts need the saemix package to run, which we haven’t included in the repository as it is freely available on the CRAN (https://cran.r-project.org/web/packages/saemix/index.html). Additional libraries we make use of in the code (MICE, tidyverse, ggplot2) also need to be installed prior to execution.  The R code provided can be further customised to be adapted to different scenarios. For the code to run, it is preferable to unzip the whole folder and set the working directory to the source file location as the script uses the "bootstrap" and "imputed_data_sets" sub-folders To execute this code, assuming the required libraries are available in the local R installation, please open an R session and run: source("Script_imputation_selection.R") # for the covariate selection method (runtime: 3h on a  i7-8565U laptop) source("script_bootstrap_indfit.R") # to obtain individual trajectories (runtime: 1h on a  i7-8565U laptop)

Learning tasks and result data for the 2024 GECCO short paper Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning

Learning tasks and result data for the 2024 GECCO short paper Length-niching Selection and Spatial Crossover in Variable-length Evolutionary Rule Set Learning by David Pätzel, Richard Nordsieck and Jörg Hähner.

Example DESC DC1 Photo-z Train/Test Set

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Data Sets For: Heterogeneous Structure, Mechanisms of Counterion Exchange, and the Spacer Salt Effect in Complex Molten Salt Mixtures Including LaCl3

Data Sets For: Heterogeneous Structure, Mechanisms of Counterion Exchange, and the Spacer Salt Effect in Complex Molten Salt Mixtures Including LaCl3

Points_Data_Set

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Data set for A Novel VNS-based Algorithm for SVC Allocation in the Brazilian Interconnected Power System

This release includes the 107-bus version of the Brazilian Interconnected Power System (available <a href="https://www.sistemas-teste.com.br/">here</a>). The system consists of 107 buses, 104 lines, and 67 transformers distributed across three areas: South, Southeast, and Mato Grosso. This test system provides extensive applications for problems related to steady-state analysis.

opencc-by-4.0Jun 2022View details →

Mnemosine Data-Set

This repository contains the OPEN-DATA information for collection Mnemosine.

Open Data Set for the article Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 2024, 6 (2), 363-382

The datasets available for open access from the article ‘Ballester, C. and Furió, D. (2024). Analysing the impact of renewables on Iberian wholesale electricity market prices using machine learning techniques. Green Finance, 6 (2), 363-382’ are provided here. This study has been supported by funding from the Spanish Ministry of Science, Innovation, and Universities (Project PGC2018-093645-B-100). The data encompasses the price series of the components of the final wholesale prices, other than the day-ahead market price, at an hourly frequency, from January 2017 to December 2021. In particular, the daily average of the hourly price series of the intraday market, which captures the net effect of the six sessions of the intraday market on the final price, the daily average of the hourly net effect on the final price of the procedure to solve technical constraints, the daily average of the hourly costs resulting from ancillary services and deviation management, the daily average of the hourly costs related to capacity payments and the daily average of the hourly costs associated with the interruptibility service. In addition, we compute the daily average of the hourly series of bids (price and amount) individually submitted by market participants to buy or sell energy, distinguishing between matched and non-matched bids, both in the day-ahead market and in the first session of the intraday market. Other energy-related price series included in the analysis are: the Dutch TTF futures price, the API2 index for the coal price, the EUA futures price, the percentage of hours with 100% use from the France-Spain interconnection, the spread from the France-Spain interconnection, the percentage of water reserves in the reservoirs of the Iberian Peninsula. All shareable data are made available in accordance with open data principles to promote transparency and reproducibility in research. However, there is a specific dataset that we are not authorized to share publicly. Specifically, this includes the data corresponding to the Dutch TTF futures price and the API2 index. Consequently, this dataset is published under restricted access.

Open Data Set for the article Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763

The datasets available for open access from the article ‘Furió, D. and Moreno-del-Castillo, J. Dynamic demand response to electricity prices: Evidence from the Spanish retail market. Utilities Policy, 88 (2024), 101763’ are provided here. The datasets comprise time series data on wholesale market global prices and quantity demanded by reference suppliers and competing retailers, along with day-ahead market prices for each of the 24 hours of the day spanning from January 1, 2007, to March 31, 2022. These series have been downloaded from the CNMC website. Additionally, primary temperature data were obtained from the Spanish Meteorological Agency’s website. This dataset provided hourly weather information from a nationwide network of weather stations. Given the scope of the demand series data, which reflects the entire Spanish market, the temperature series were constructed to ensure national representativeness. These data were then aggregated to develop comprehensive national temperature series, aligning them with the demand series. Finally, a dummy variable for each day t of the studied period was constructed to capture the business/non-business day effect on electricity demand. It takes the value of 1 if 𝑡 corresponds to a working day (non-holiday, Monday to Friday) and 0 otherwise. The national holidays considered are as follows: January 1st, January 6th, May 1st, August 15th, October 12th, November 1st, December 6th, December 8th, and December 25th. Additionally, the corresponding Good Friday for each year included in the study period was also considered.

Data set consisting of proteins with associated hydration sites calculated by WATsite

The data set contains 3793 proteins. Each protein is provided as "protein_sasa.pdb", also specifying the SASA value per atom.  For each protein, the hydration sites with associated occupancy and thermodynamic properties were calculated using WATsite and are provided in the files "watsite.csv".

E-commerce data set

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Supplementary Data Sets for Machine Learning Tracks the Role of Slab-Derived Fluids in Large Igneous Province/CUGBZL

Supplementary Dataset S1:  All data collcted from both the GEOROC database and recent literature published in Chinese and English journals and books. Supplementary Dataset S2: Data after cleaning. Supplementary Dataset S3: Prediction results.

DATA SET: Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies

This repository contains the data sets of the article: P. Fernández Esteberena et al. (2024). Near-infrared diffuse optical characterization of human thyroid using ultrasound-guided hybrid time-domain and diffuse correlation spectroscopies. Biomedical Optics Express. <a href="https://doi.org/10.1364/BOE.538141">https://doi.org/10.1364/BOE.538141</a>

Bread wheat response to saline stress across four experimental settings

Yield, growth and physiological response of bread wheat to saline stress in raised beds outdoors, pots outdoors, greenhouse and climate chamber. All experiments were conducted on spring wheat (Triticum aestivum) cv. Tybalt (Limagrain, Saint Beauzire, France, https://www.limagrain.com/). Seeds were sown in wet compost in individual wells of a germination tray in a greenhouse and transplanted to the different settings a week after sowing. The greenhouse pots (GH), outdoors pots (OP) and raised bed (RB) experiments took place from February to July 2022 in the experimental “Skardon” garden at the University of Plymouth, Plymouth, United Kingdom (50° 37’ N 4° 14’ W), all within 15 m of each other. The four raised beds and OP experiment were located next to each other (with the pots equally split in four groups at the edge of each raised bed under a rain out shelter in the form of a polytunnel that could be opened and closed manually. The shelter was open for the majority of the experiment, so the plants experienced natural outdoors conditions; it was closed shortly before heavy precipitation to avoid rainwater draining the salt from the pots or too deep in the soil of the raised beds. Caution was taken for the shelter to always be open under sunny weather to avoid creating a greenhouse effect artificially increasing temperature. The climate chamber (CC) experiment was conducted in a Sanyo growth chamber (photosynthetically active radiation 183 µmol m-2 s-1) after the other experiments were completed. Photoperiod and day and night temperatures were adjusted weekly to match the average of the conditions in the GH experiment. For CC, GH and OP, three seedlings were transplanted in 3 L black plastic square pots (15 cm x 15 cm at the top and 20 cm high) containing 3 kg of a mix of 50% Devon farming soil, 40% soil from the experimental garden and 10% compost (Peat Free Multi-purpose with added John Innes, Westland Horticulture Ltd., Dugannon, Northern Ireland, UK). Mixing of the different components of the growth medium was carried out in the top 50 cm of the raised beds and pots were filled with that same medium. Pots were saturated with water and left to drain to establish full pot water capacity. For the RB experiment, seedlings were transplanted at a density of 220 plants m-2, which is within the range of density for wheat in the UK. Pots and raised beds were manually weeded as necessary. Fertilisation was provided in the form of a 0.75% dilution of Wilko® lawn feed (15-3-3 NPK with iron), two doses of 50 mL per pot or 2 L per raised bed (equivalent volume per surface) during the vegetative stage.   Stress treatment In all experiments, the salt stress treatment started at the two tiller-stage, approximately a month after sowing, with the exact time varying slightly between experiments (between 28 and 36 days after sowing), as seedlings grew faster in GH and CC. Salt stress was applied using a solution of sea salts (Instant Ocean®, Blacksburg, VA, USA, https://www.instantocean.com/) that represents better the mix of ions found in brackish water and saline soils than pure NaCl, dissolved in rain water for RB, OP and GH, and in distilled water for CC; rain water and distilled water were also used for control watering. Each pot was watered with 50 mL of salt solution and each raised bed with 2 L of salt solution, which amounted to the same volume per surface three times a week. The concentration of salt was gradually increased by 2 g L-1 increments over three weeks until the final concentration of 22 g L-1 (27 dS m-1) was reached. This was done to mimic the slow increase in soil solution salinity that takes place during the growing season in saline agricultural fields and avoid osmotic shock (Shavrukov, 2013). The salt stress treatment was maintained until there was no more green leaf tissue visible in stressed plants, which amounted to 92 days of treatment for GH, 94 days for CC and OP and 110 days for RB. Soil water content was maintained at 70% pot water capacity in both control and stressed pots from transplanting until all plants in a pot had no more green tissue visible. Comparable soil moisture was obtained in the RBs as in the pots using a moisture meter (HH2 ThetaProbe, Delta-T Devices Ltd, Cambridge, UK, <a href="http://www.delta-t.co.uk">www.delta-t.co.uk</a>). Soil salinity was measured at the end of the experiment at different depths for the salt treatments using the EC1:5 method (He et al., 2012). For pots, soil samples were taken from the first two top cm, 5 to 7 cm deep and 12 to 14 cm deep (bottom of the pot); for the raised beds, soil samples were taken from the first two top cm, 4 to 9 cm deep, 10 to 14 cm deep and 23 to 28 cm deep in at least two replicates for each setting. The baseline EC1:5 value for the soil used in the experiment was 0.35 dS m-1. Similar results were obtained for the pots in all three settings, with EC1:5 values around 21 dS m-1 for the top two cm of soil, 3.5 dS m-1 at middle depth and 7.5 dS m-1 at the bottom of the pot. For the raised beds, the top two cm had an EC1:5 of 6.8 dS m-1, 2.4 dS m-1 at 4 to 9 cm deep, 1.4 dS m-1 at 10 to 14 cm deep and 1.2 dS m-1 23 to 28 cm deep.   Plant sampling, growth and yield measurements Plant height (from the soil to the end of the longest leaf) was recorded weekly from the start of the stress treatment for six randomly selected plants per treatment in GH and CC, for eight plants in GP and 16 plants in RB. More plants were measured for OP and RB to account for the added variability created by the split into two blocks per treatment and the highest variability observed for plant size in RB. Four plants per setting and treatment were harvested at three different times during the experiment: after 23 to 25 days of stress (tillering stage for CC, OP and RB, stem elongation stage for GH), after 29 to 31 days of stress (tillering stage for CC, end of tillering stage for OP and RB, stem elongation stage for GH) and after 46 to 49 days of stress (tillering stage for CC, start of booting stage for OP and RB, end of booting stage for GH). Whole shoots were harvested, rinsed to remove salt from stress treatment watering, with the ears removed when they were present and the shoots without ears were frozen in liquid nitrogen. Sampling was always carried out around 9 am to avoid circadian variations in metabolites between sampling points. For CC, GH and OP only one plant per pot was harvested over the three sampling times for each pot, so at maturity there were two plants in each pot. Harvesting was done once plants had reached maturity. For CC, GH and OP all remaining plants (18 per treatment, minus six for OP control and four for OP salt because of an undiagnosed disease that affected yield); for RB 30 plants (none from the edge of the plot and none with the same undiagnosed disease as some GP plants) per treatment were randomly harvested. Plants were dried in an oven overnight at 60°C to evaporate any remaining humidity. Shoot biomass was recorded, and for each plant, spikes and grains were counted and weighed.   Shoot tissue analyses Whole shoots were freeze dried for at least 48 h, ground into a fine powder using a coffee grinder and stored in dry conditions. Sodium and potassium were quantified using flame photometry. The method was adapted from (Munns et al., 2010). About 15 mg of tissue powder was digested in 2 mL of 0.5 M nitric acid for one hour at 80°C. After 10 min of centrifugation, the supernatant was collected and diluted 5 times (control samples only for sodium analysis) and 50 times. Diluted samples and standards were quantified using a flame photometer (Model 420, Sherwood, Cambridge, UK, sherwood-scientific.com). Proline quantification was adapted from (Shabnam et al., 2016). Twelve to 15 mg of tissue powder were extracted with 40% ethanol, 10 mM ascorbate for 18 h at 4°C. After 10 min of centrifugation, 33 µL of the supernatant was mixed with 66 µL of 1.25% w/v ninhydrin in glacial acetic acid in 200 µL PCR tubes and incubated at 100°C for 30 min. This was replicated for each sample replacing the ninhydrin reagent with glacial acetic as a “sample blank” to assess the baseline colouration (independent of the ninhydrin reacting with proline) of each sample. The content of each tube was transferred to a 96 well plate and absorbance at 520 nm was measured in a plate reader (Spectramax Plus 384, Molecular Devices, San Jose, CA, USA, moleculardevices.com/). Proline concentration was calculated using the values for standards included in each set of reactions and by subtracting the absorbance value for the “sample blank”. Chlorophyll extraction was modified from Sims and Gamon (2002). About 12 mg of dry leaf powder was extracted twice with 1.8 mL of 80:20 (v/v) acetone Tris buffer (pH 7.8), shaking for 5 min, followed by 5 min of centrifugation. Absorbance was measured at 537, 647 and 663 nm and chlorophyll concentration was calculated using the equations provided by Sims and Gamon (2002).

opencc-by-4.0Sep 2024View details →

Annotated Cows in Aerial Images - Test Set for Livestock Detection Models

This dataset contains a test set of aerial images from fields in Juchowo, Poland and Wageningen, the Netherlands, with annotated cows. The annotations are provided in a CSV format containing image file paths, bounding box coordinates (<code>xmin</code>, <code>ymin</code>, <code>xmax</code>, <code>ymax</code>), and labels for the objects (cows). The dataset is intended for evaluating livestock detection models in deep learning, particularly DeepForest. The images and their corresponding bounding box annotations are included in this archive. The test set consists of 10% of the total images, split from the original dataset created by G.J. Franke and Sander Mucher, which is available in [Harvard Dataverse](https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/N7GJYU). This dataset is part of the GenTORE project (<a href="https://www.gentore.eu" target="_new" rel="noopener">https://www.gentore.eu</a>), focusing on precision livestock farming using automated detection and deep learning techniques. Contributions Notebook <ul> <li> Alejandro Coca-Castro (author), The Alan Turing Institute, <a href="https://github.com/acocac">@acocac</a> </li> </ul> Dataset reference and documentation <ul> <li> Franke, G.J., & Mucher, S. (2021). Annotated cows in aerial images for use in deep learning models. Harvard Dataverse. <a href="https://doi.org/10.7910/DVN/N7GJYU" target="_new" rel="noopener">https://doi.org/10.7910/DVN/N7GJYU</a> </li> </ul>

Data Set For GGA24 a non-decomposable approximation for a non-additive kinetic potential

Funding: National Science Center of Poland under grants no. 2023/07/X/ST4/01357 MINIATURA7.