Find research datasets worth reusing

opencc-by-4.0Oct 2024View details →

An Audit of Machine Learning Experiments on Software Defect Prediction - Dataset

ML_Audit_20250328_anon.csv: This CSV file contains anonymized data used in the audit of machine learning experiments on software defect prediction. The dataset includes variables and performance metrics extracted from studies published between 2019 and 2023. It supports the audit's evaluation of study reproducibility and issues related to experimental design and statistical analysis. This data can be used for replication and further analysis of the trends and reproducibility issues identified in the paper. ML_Audit_March2025.Rmd: This RMarkdown file contains the analysis script used for the statistical analysis and audit of the machine learning experiments reviewed in the study. It includes the procedures for data preprocessing, statistical evaluations, and reproducibility assessments. The script is integral for replicating the audit results presented in the paper and can be used by other researchers to perform similar audits or extend the analysis on different datasets.

opencc-zeroOct 2021View details →

Bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting as predicted by the Madingley General Ecosystem Model

The datasets contain data generated using the Madingley General Ecosystem Model for experiments decribed in the paper: "T. Barychka, G.M.Mace and D.W.Purves (2021) The Madingley General Ecosystem Model predicts bushmeat yields, species extinction rates and ecosystem-level impacts of bushmeat harvesting. Oikos." The Madingley General Ecosystem Model was used to generate predictions of bushmeat yields, extinction rates and broader ecosystem impacts for a range of harvesting intensities of duiker-sized endothermic herbivores. Duiker antelope (such as Cephalophus callipygus and Cephalophus dorsalis) are the most heavily hunted species in sub-Saharan Africa, contributing 34%-95% of all bushmeat in the Congo Basin. In the Madingley, the harvested group was described as "Heterotroph – Herbivore – Terrestrial – Mobile – Iteroparous– Endotherm", with adult bodymasses of 13-21 kg and juvenile bodymasses of >100 g. Harvesting period was set at 30 years (n =30). In the first experiment, we used the Madingley model to predict bushmeat yields ("Harvested Biomasses") and extinction rates ("Density") from harvesting duiker-sized herbivores using proportional harvesting strategy, with harvest rates ranging from 0 to 0.90. These were compared to the estimates of bushmeat yields and survival probabilities for two duiker antelope species (Cephalophus callipygus and Cephalophus dorsalis) from conventional single-species Beverton-Holt model. In the second experiment, we used the Madingley model to generate data on the state ("State") of the harvested ecosystem. The datasets contain estimates of biomasses, abundances, adult and juvenile bodymasses, etc. of the harvested duiker-sized herbivores as well as unharvested herbivores, omnivores and carnivores present in the simulated ecosystem. In the paper we focused on abundances; however, other estimates e.g., adult bodymasses can be used to conduct further studies on the effects of harvesting in tropical ecosystems. Main results of the experiments are that: 1) the Madingley model gave estimates for optimal harvesting rate, and extinction rate, that were qualitatively and quantitatively similar to the estimates from conventional single-species Beverton-Holt model; 2) the Madingley model predicted a background local extinction probability for the target species of at least 10%; 3) at medium and high levels of harvesting of duiker-sized herbivores, the Madingley model predicted statistically significant, but moderate, reductions in the densities of the targeted functional group; increases in small-bodied herbivores; decreases in large-bodied carnivores; and minimal ecosystem-level impacts overall.

opencc-by-4.0Oct 2021View details →

Archival bundle of the data used for "Predictive Auto-scaling with OpenStack Monasca" (UCC 2021)

This archive contains the data used for the paper Predictive Auto-scaling with OpenStack Monasca <a href="mailto:giacomo.lanciano@sns.it">Giacomo Lanciano</a>*, Filippo Galli, Tommaso Cucinotta, Davide Bacciu, Andrea Passarella 2021 IEEE/ACM 14th International Conference on Utility and Cloud Computing (UCC) <a href="https://doi.org/10.1145/3468737.3494104">10.1145/3468737.3494104</a> Follow the instructions provided in the <a href="https://github.com/giacomolanciano/UCC2021-predictive-auto-scaling-openstack">companion repo</a> to automatically download and decompress the archive. The following files are included: <table> <tbody> <tr> <td>File</td> <td>Description</td> </tr> <tr> <td> amphora-x64-haproxy.qcow2 </td> <td> Image used to create Octavia amphorae </td> </tr> <tr> <td> distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>.log </td> <td> distwalk run log </td> </tr> <tr> <td> distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-pred.json </td> <td> Predictive metric data exported from Monasca DB </td> </tr> <tr> <td> distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-real.json </td> <td> Actual metric data exported from Monasca DB </td> </tr> <tr> <td> distwalk-{lin,mlp,rnn,stc}-<INCREMENTAL-ID>-times.csv </td> <td> Client-side response time for each request sent during a run </td> </tr> <tr> <td> model_dumps/* </td> <td> Dumps of the models and data scalers used for the validation </td> </tr> <tr> <td> predictor.log </td> <td> monasca-predictor log </td> </tr> <tr> <td> predictor-times.log </td> <td> monasca-predictor` log (timing info only) </td> </tr> <tr> <td> predictor-times-{lin,mlp,rnn}.{csv,log} </td> <td> monasca-predictor log (timing info only, group by predictor) </td> </tr> <tr> <td> super_steep_behavior.csv </td> <td> Dataset used to train MLP and RNN models </td> </tr> <tr> <td> test_behavior_02_distwalk-6t_last100.dat </td> <td> distwalk load trace </td> </tr> <tr> <td> ubuntu-20.04-min-distwalk.img </td> <td> Image used to create Nova instances for the scaling group </td> </tr> </tbody> </table> * contact author

opencc-by-4.0Oct 2021View details →

Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)

Fine-tuning of predictive microbiology models through microlocal characterization of foods by Nuclear Magnetic Resonance (NMR)

opencc-zeroNov 2021View details →

Genomic prediction in the wild: a case study in Soay sheep

Genomic prediction, the technique whereby an individual's genetic component of their phenotype is estimated from its genome, has revolutionised animal and plant breeding and medical genetics. However, despite being first introduced nearly two decades ago, it has hardly been adopted by the evolutionary genetics community studying wild organisms. Here, genomic prediction is performed on eight traits in a wild population of Soay sheep. The population has been the focus of a >30 year evolutionary ecology study and there is already considerable understanding of the genetic architecture of the focal Mendelian and quantitative traits. We show that the accuracy of genomic prediction is high for all traits, but especially those with loci of large effect segregating. Five different methods are compared, and the two methods that can accommodate zero-effect and large-effect loci in the same model tend to perform best. If the accuracy of genomic prediction is similar in other wild populations, then there is a real opportunity for pedigree-free molecular quantitative genetics research to be enabled in many more wild populations; currently the literature is dominated by studies that have required decades of field data collection to generate sufficiently deep pedigrees. Finally, some of the potential applications of genomic prediction in wild populations are discussed.

opencc-by-4.0Mar 2019View details →

Liver and Blood Expression Prediction Generated for Opportunities and challenges for transcriptome-wide association studies

db files are in sqlite format and contain weights to calculate genetic predictors of gene expression Blood, Liver indicate the tissue where the RNA was extracted from Crohns and LDL indicate that prediction models were generated using SNPs available in the GWAS summary results used in the paper (Wainberg et al <a href="https://doi.org/10.1038/s41588-019-0385-z">https://doi.org/10.1038/s41588-019-0385-z</a>) *txt.gz filex contain covariances between SNPs needed to run the summary version of PrediXcan or other TWAS methods Information about the RNAseq data can be found here https://pubmed.ncbi.nlm.nih.gov/27540175/ Sample size n=522

opencc-by-4.0Nov 2021View details →

Random Forest model for PPI predictions

The uploaded model contains the trained random forest model to predict protein-protein Interactions based on their amino acid sequence. The model is described in our preprint "ProteinPrompt: a webserver for predicting protein-protein interactions" which can be found on bioRxiv.org: https://doi.org/10.1101/2021.09.03.458859 The model was trained with the scikit-learn package in version 0.20.3 under Python 3.7.12

opencc-by-4.0Nov 2021View details →

molxspec: Deep learning models for predicting MS2 spectra from molecular structures

This repository contains a pre-processed dataset derived from the <a href="https://gnps.ucsd.edu">GNPS public repository</a> of natural product mass spectra as well as pretrained model weights for four different types of model architectures using pytorch (version 1.9.0). The contents are as follows: <ul> <li>gnps_processed_data.tgz: Contains tab separated files of molecule/MS2 spectra pairs derived from GNPS after filtering for invalid structures, too large molecules (bigger than 2000 M/Z spectra), and structures that yielded valid 3D geometry optimization. The processing steps were done for positive ionization mode (pos_* files), though negative ionization data is also included (neg_* files)</li> <li>models.tgz: Contains pytorch format pretrained models for four different architecutres: MLP (a residual block multilayer perceptron trained on ECFP molecular fingerprints), BERT (the same MLP but trained on pretrained representations from the Zinc V1 pretrained ChemBERTa models on SMILES), GCN (a graph convolution architecture), and EGNN (an equivariant graph neural network). Models were trained on pos_processed_gnps_shuffled_with_3d_train.tsv found in the gnps_processed_data.tgz file described previously.</li> </ul>

opencc-by-4.0Mar 2013View details →

Data release for "The T2K Neutrino Flux Prediction" (2013)

This data release is superseded by the<a href="https://zenodo.org/record/5734267#.YaYEsFOnxsE"> flux release in 2016</a>. The files in the following archive file contain the neutrino beam flux predictions for the T2K ND280 (near) and Super-Kamiokande (far) detectors. The description of the flux predictions is published in <a href="https://journals.aps.org/prd/abstract/10.1103/PhysRevD.87.012001">Phys. Rev. D.87.012001</a> (<a href="https://arxiv.org/abs/1211.0469">arxiv.org:1211.0469 [hep-ex]</a>). The provided flux predictions include no neutrino oscillations. This tar file contains the flux predictions as well as a README.pdf file with detailed information on the included files.

opencc-by-4.0Nov 2021View details →

Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies

The pcYeast7.6 model and files, required to reproduce the figures, provided in the publication "Whole-cell modeling in yeast predicts compartment-specific proteome constraints that drive metabolic strategies", accepted in Nat Commun. The Zenodo upload was created by Pranas Grigaitis, p.grigaitis [at] vu.nl. Abstract When conditions change, unicellular organisms rewire their metabolism to sustain cell maintenance and cellular growth. Such rewiring may be understood as resource re-allocation under cellular constraints. Eukaryal cells contain metabolically active organelles such as mitochondria, competing for cytosolic space and resources, and the nature of the relevant cellular constraints remain to be determined for such cells. Here we present a comprehensive metabolic model of the yeast cell, based on its full metabolic reaction network extended with protein synthesis and degradation reactions. The model predicts metabolic fluxes and corresponding protein expression by constraining compartment-specific protein pools and maximising growth rate. Comparing model predictions with quantitative experimental data suggests that under glucose limitation, a mitochondrial constraint limits growth at the onset of ethanol formation - known as the Crabtree effect. Under sugar excess, however, a constraint on total cytosolic volume dictates overflow metabolism. Our comprehensive model thus identifies condition-dependent and compartment-specific constraints that can explain metabolic strategies and protein expression profiles from growth rate optimization, providing a framework to understand metabolic adaptation in eukaryal cells.

opencc-zeroNov 2021View details →

MetaComNet: A random forest-based framework for making spatial prediction of plant-pollinator interactions

1. Predicting plant-pollinator interaction networks over space and time will improve our understanding of how environmental change is likely to impact the functioning of ecosystems. Here we propose a framework for producing spatially explicit predictions of the occurrence and number of pairwise plant-pollinator interactions and of the species richness, diversity, and abundance of pollinators visiting flowers. We call the framework 'MetaComNet' because it aims to link metacommunity dynamics to the assembly of ecological networks. 2. To illustrate the MetaComNet functionality, we used a dataset on bee-flower networks sampled at 16 sites in southeast Norway along with random forest models to predict bee-flower interactions. We included variables associated with climatic conditions (elevation) and habitat availability within a 250m radius of each site. Regional commonness, site-specific distance to conspecifics, social guild, and floral preference were included as bee traits. Each plant species was assigned a score reflecting its site-specific abundance, and four scores reflecting the bee species that the plant family is known to attract. We used leave-one-out cross-validations to assess the models' ability to predict pairwise plant-bee interactions across the landscape. 3. The relationship between observed occurrence or absence of interactions and the predicted probability of interactions was nearly proportional (GLMlogistic regression slope = 1.09), matching the data well (AUC = 0.88), and explained 30% of the variation. Predicted probability of interactions was also correlated with the number of observed pairwise interactions (r = 0.32). The sum of predicted probabilities of bee-flower interactions were positively correlated with observed species richness (r = 0.50), diversity (r = 0.48), and abundance (r = 0.42) of wild bees interacting with plant species within sites. 4. Our findings show that the MetaComNet framework can be a useful approach for making spatially explicit predictions and mapping plant-pollinator interactions. Such predictions have the potential to identify areas where the pollination potential for wild plants is particularly high, and where conservation action should be directed to preserve this ecosystem function.

Dataset: Improving the prediction of fertilizer phosphorus availability to plants with simple, but non-standardized extraction techniques

Dataset: Improving the prediction of fertilizer phosphorus availability to plants with simple, but non-standardized extraction techniques

opencc-zeroDec 2021View details →

Effects of enhanced productivity of resources shared by predators in a food-web module: Comparing results of a field experiment to predictions of mathematical models of intra-guild predation

This dataset contains data from a field experiment described in the publication "Wise, D. H. & Farfan, M.A. (2021) Effects of enhanced productivity of resources shared by predators in a food-web module: Comparing results of a field experiment to predictions of mathematical models of intra-guild predation. Ecology and Evolution, 00: 1-11. <a href="https://doi.org/10.1002/ece3.8375">https://doi.org/10.1002/ece3.8375</a>". The field experiment compared the response to increased input of nutrients and energy (artificial detritus) to an empirical model of intra-guild predation (IGP) to the predictions of published, simple mathematical models of asymmetric IGP (a generalist IG Predator that feeds both on a specialist IG Prey and a Resource that it shares with the IG Prey). The empirical model was a food-web module created by pooling species abundances across many families in a community of soil micro-arthropods into three response variables: IG Predator (large predatory mites), IG Prey (small predatory mites) and a shared Resource (fungivorous mites and springtails). The pattern of change over time in densities of the three response variables (IG Predator, IG Prey and Resource) was compared to the predictions of mathematical models of IGP to determine if the feeding relationships in this community of soil micro-arthropods could be abstracted into a simple IGP module. Thus, we were testing the hypothesis that IGP is a dominant organizing principle in this community. Simple mathematical models predict that increased input of nutrients and energy to the shared Resource will increase the equilibrium density of Resource and IG Predator, but will decrease that of IG Prey. By the experiment's end, densities of fungivores (Resource) had increased ~1.5x (ratio of pooled fungivore densities in the High treatment to plots with no addition of detritus (None treatment); and IG Predator densities had increased ~4x. Contrary to the prediction of mathematical models, IG Prey had not decreased, but instead had increased ~1.5x. We discuss possible reasons for the failure of the empirical model to agree with IGP theory.

opencc-zeroDec 2021View details →

Both source and recipient range phylogenetic community structure can predict the outcome of avian introductions

Competing phylogenetic models have been proposed to explain the success of species introduced to other communities. Here, we present a study predicting the establishment success of birds introduced to Florida, Hawaii, and New Zealand using several alternative models, considering species' phylogenetic relatedness to source and recipient range taxa, propagule pressure, and traits. We find consistent support for the predictive ability of source region phylogenetic structure. However, we find that the effects of recipient region phylogenetic structure vary in sign and magnitude depending on inclusion of source region phylogenetic structure, delineation of the recipient species pool, and the use of phylogenetic correction in the models. We argue that tests of alternative phylogenetic hypotheses including the both source and recipient community phylogenetic structure, as well as important covariates such as propagule pressure, are likely to be critical for identifying general phylogenetic patterns in introduction success, predicting future invasions, and for stimulating further exploration of the underlying mechanisms of invasibility.

Data from: Better together? Assessing different remote sensing products for predicting habitat suitability of wetland birds

This data repository contains the processed and extracted metrics from the Dutch land cover, country wide airborne laser scanning and Sentinel-1 and 2 datasets used as input predictor variables in  the species distribution modelling step. The study area within the Netherlands comprised five Dutch provinces (Groningen, Drenthe, Overijssel, Gelderland, and Flevoland) for which both ALS and Sentinel data were available for the same year. The land cover metrics were derived using the Dutch land cover map from 2018 (LGN2018 or LGN8). The country-wide LiDAR point clouds were derived from the third Dutch national ALS flight campaign (AHN3, Actueel Hoogtebestand Nederland). The AHN3 dataset is openly accessible data available from (<a href="https://ahn.arcgisonline.nl/ahnviewer/">https://ahn.arcgisonline.nl/ahnviewer/</a>). The Sentinel datasets were processed using Google Earth Engine.

Supplementary materials for "Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene–gene associations"

This repository contained supplementary materials in the study named: "Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene–gene associations." For extracting all files from the downloaded tar.gz file, the following commanda could be used: <pre><code>tar -xf supplementary_materials.tar.gz </code></pre> The supplementary_materials/data diretcory contains the following sections: <ol> <li>eqtm_predictions: This contains the training and testing datasets for the eQTM prediction procedures</li> <li>public_methylation_data_and_pca: This contains the harmonized public DNA methylation dataset and its first 100 PCA components</li> <li>cca_data: This contains the CCA components for the public DNA methylation and gene expression datasets for the negative eQTMs, and the input datasets for the functional gene pair prediction analylsis.</li> <li>gene_enrichment_results_for_cca: This contains the gene enrichment results for the CCA components for negative and positive eQTMs</li> </ol> The supplementary_materials/model directory contains the following sections: <ol> <li>disease_tissue_predictions: This contains the models trained for tissue prediction and disease prediction based on the PCA components from the public DNA methylation data</li> <li>eqtm_predictions: This contains models trained for eQTM prediction</li> <li>cca_transformations: This contains CCA transformation models and models for STRING gene pair predictions  </li> </ol>

DNAPL_MassDischa_Prediction

The data and the codes used in paper "Integration of deep learning-based inversion and upscaled mass-transfer model for DNAPL mass-discharge prediction and uncertainty assessment"

openDec 2021View details →

Dataset: "Vibronic Coupling in Spherically Encapsulated, Diatomic Molecules: Prediction of a Renner-Teller-like Effect for Endofullerenes"

This datasets contains scripts and output files for the publication:                            "Vibronic coupling in spherically encapsulated, diatomic molecules:                                   Prediction of a Renner-Teller-like effect for endofullerenes" by Andreas W. Hauser and Johann V. Pototschnig The zip file NO.zip contains the output files. The zip file code.zip contains the python scripts.

Predicted peptides: Another lesson from unmapped reads – in depth analysis of RNA-Seq reads from various horse tissues

Predicted peptides – peptides predicted based on the assembled transcripts using TransDecoder software   Data: loin adipose - AD, hoof lamina - LM, liver - LI, longissimus muscle - LO, left lung - LU, heart left ventricle - LV, ovary- OV and parietal cortex – PC 1 – horse ECA_UCD_AH1 2 – horse ECA_UCD_AH2

Example data for chromatin interaction predictions using deepC.

This archive contains example files, data and models to run deepC predictions of chromatin interactions from DNA sequence, specifically the tutorials and example commands described in the deepC repository. https://github.com/rschwess/deepC https://github.com/rschwess/deepHaem Copies of the deepC and deepHaem cover are included for continuity. Please see the deepC publication for details: Schwessinger, R., Gosden, M., Downes, D. et al. DeepC: predicting 3D genome folding using megabase-scale transfer learning. Nat Methods 17, 1118–1124 (2020). https://doi.org/10.1038/s41592-020-0960-3 Human Hi-C is based on Rao, S. S. P. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014). Mouse Hi-C is based on Bonev, B. et al. Multiscale 3D genome rewiring during mouse neural development. Cell 171, 557–572.e24 (2017). DNase-seq and CTCF ChIP-seq were retrieved from the ENCODE Data portal (https://www.encodeproject.org/) Bernstein, B. E. et al. An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).