Find research datasets worth reusing

Dataset for Peptide binder design with inverse folding and protein structure prediction

Dataset for a paper on peptide design   mutated_peptides - results for randomly intriduced mutations in protein-peptide complexes that can be predicted at 2 Å (Figure 1) pdb_peptide  - variation in the number of recycles (1-10) for 96 peptides (Figure 1) minibinder - results for the minibinder set (Figure 2) Pfam - results for the Pfam set (Figures 4+5) protein_mpnn - results on protein_mpnn test set (Figure 6)

opencc-zeroOct 2023View details →

dryad36/100

Gene prediction for: A reference genome for ecological restoration of the sunflower sea star, Pycnopodia helianthoides

<div> <div> <div> <div>Wildlife diseases, such as the sea star wasting (SSW) epizootic that outbroke in the mid-2010s, appear to be associated with acute and/or chronic abiotic environmental change; dissociating the effects of different drivers can be difficult. The sunflower sea star, Pycnopodia helianthoides, was the species most severely impacted during the SSW outbreak, which overlapped with periods of anomalous atmospheric and oceanographic conditions, and there is not yet a consensus on the cause(s). Genomic data may reveal underlying molecular signatures that implicate a subset of factors and, thus, clarify past events while also setting the scene for effective restoration efforts. To advance this goal, we used Pacific Biosciences HiFi long sequencing reads and Dovetail Omni-C proximity reads to generate a highly contiguous genome assembly that was then annotated using RNA-seq-informed gene prediction. The genome assembly is 484 Mb long, with contig N50 of 1.9 Mb, scaffold N50 of 21.8 Mb, BUSCO completeness score 96.1%, and 22 major scaffolds consistent with prior evidence that sea star genomes comprise 22 autosomes. These statistics generally fall between those of other recently assembled chromosome-scale assemblies for two species in the distantly related asteroid genus Pisaster. These novel genomic resources for Pycnopodia helianthoides will underwrite population genomic, comparative genomic, and phylogenomic analyses — as well as their integration across scales — of SSW and environmental stressors. This data resource contains the files associated with gene prediction.</div> </div> </div> </div>

openmit-licenseOct 2023View details →

Data from Genome scale metabolic network modelling for metabolic profile predictions

Data used to produce figures 4, 5 and 6 in the paper Genome scale metabolic network modelling for metabolic profile predictions.

Dataset for: Windows of opportunity for predicting seasonal climate extremes: Pakistan floods of 2022

Data required to regenerate any of the figures in Nature Communications paper "Windows of opportunity for predicting seasonal climate extremes: Pakistan floods of 2022"

Dataset for: "Skilful predictions of the Summer North Atlantic Oscillation"

Met Office DePreSys3 climate prediction data used in the generation of the figures in the paper "Skilful predictions of the Summer North Atlantic Oscillation" submitted to Communications Earth & Environment.

opencc-by-4.0Jun 2023View details →

Data sets and machine learning models for: Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates

The datasets and final machine learning model files for the manuscript "Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates". Citation should refer directly to the manuscript: <ul> <li>Chung, Y.; Green, W. H. Machine learning from quantum chemistry to predict experimental solvent effects on reaction rates. Chemical Science 2024, doi: <a href="https://doi.org/10.1039/D3SC05353A">10.1039/D3SC05353A</a></li> </ul> To use the machine learning models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a>.  Detailed information can be found in README.md file. Details on the files In the pretraining and finetuning set csv files, each column represents: <ol> <li>rxn_smiles: atom-mapped reaction SMILES</li> <li>solvent_smiles: solvent SMILES</li> <li>ddGsolv: solvation free energy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>ddHsolv: solvation enthalpy of activation of a reaction-solvent pair at 298K in kcal/mol (main prediction target)</li> <li>dGsolv_reactant: solvation free energy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dGsolv_product: solvation free energy of product(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_reactant: solvation enthalpy of reactant(s) at 298K in kcal/mol (additional feature)</li> <li>dHsolv_product: solvation enthalpy of product(s) at 298K in kcal/mol (additional feature)</li> </ol> Data sets under 'RxnSolvKSE_dataset_v1.1.zip' <ul> <li>pretraining_set: contains the dataset used for pre-training <ul> <li>all_data: contains all calculated data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: contains both main prediction targets and additional feature for reaction-solvent pairs</li> <li>pretraining_solvent_info.csv: list of all solvents</li> <li>pretraining_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_500k_data: contains the chosen 500k data <ul> <li>pretraining_rxn_solvent_ddGsolv_ddHsolv_500k.csv: contains main prediction targets for reaction-solvent pairs</li> <li>pretraining_features_react_prod_dGsolv_dHsolv_500k.csv: contains additional features for reaction-solvent pairs</li> <li>train_test_split: contains the 5-fold random split training and test sets.</li> </ul> </li> </ul> </li> <li>finetuning_set: contains the dataset used for fine-tuning <ul> <li>all_data: contains all calculated data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_with_features_all.csv: constains both main prediction targets and additional features for reaction-solvent pairs. The rxn_key column indicates whether the reaction is bimolecular hydrogen abstraction (bihabs), unimolecular hydrogen migration (intrahabs), or radical addition to a multiple bond (raddition). The 'fwd' and 'rev' each indicate forward and reverse reactions.</li> <li>finetuning_solvent_info.csv: list of all solvents</li> <li>finetuning_unique_rxn.csv: list of all reactions, both forward and reverse directions</li> </ul> </li> <li>chosen_data: contains chosen data <ul> <li>finetuning_rxn_solvent_ddGsolv_ddHsolv_chosen.csv: contains main prediction targets for reaction-solvent pairs</li> <li>finetuning_features_react_prod_dGsolv_dHsolv_chosen.csv: contains additional features for reaction-solvent pairs</li> </ul> </li> </ul> </li> <li>experimental_set: contains the experimental rate constant data used to test the model. The original experimental data can be found at <a href="../record/7747557">https://zenodo.org/record/7747557</a>. <ul> <li> expt_rxn_atom_mapped_smiles.csv: contains the atom-mapped reaction SMILES used for the experimental data.</li> <li>expt_data_collected.xlsx: contains all experimental data and detailed information</li> <li>expt_rxn_solv_smiles_with_features_all.csv: contains the computed additional features for the experimental reaction-solvent pairs.</li> </ul> </li> </ul> Machine learning model files under 'RxnSolvKSE_ML_model_files.zip' <ul> <li>Contains the Chemprop machine learning model files for predicting ddGsolv and ddHsolv for a reaction-solvent pair. It takes atom-mapped reaction SMILES and solvent SMILES as inputs.</li> <li>To use these ML models, please refer to the sample files and instructions on <a href="https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML">https://github.com/yunsiechung/chemprop/tree/RxnSolvKSE_ML</a></li> </ul>

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Data from: Sexually attractive traits predict predation-threat sensitivity of male alternative mating tactics

Although visual sexual signals, such as ornamental colors and courtship displays, and large body size in males are attractive to females in numerous species, they may also inadvertently attract the attention of eavesdropping predators and thus may be costly in terms of increasing individual risk of mortality to predation. Theoretically, more color-ornamented and larger males should be more predation threat sensitive and suppress their sexual signaling and(or) mating effort relatively more than their less color-ornamented and smaller counterparts when under predation hazard. Here, we experimentally tested this hypothesis by quantifying concurrently the rates of alternative mating tactics (courtship displays, sneak mating attempts) expressed by male Trinidadian guppies (Poecilia reticulata) varying in color ornamentation and body size under a staged immediate threat of predation. Males suppressed their overall mating effort in response to the perceived predation threat, decreasing the frequency of their (presumably more conspicuous) courtship displays significantly more on average than the frequency of their sneak mating behavior. Statistically controlling for body length, more color-ornamented males were more threat-sensitive in their courtship displays, but not sneak mating attempts, under predation hazard than drabber males. Controlling for body coloration, larger males exhibited lower courtship and sneak mating efforts than smaller males in both predation treatments, but body length only influenced threat sensitivity in sneak mating behavior. These results are consistent with both the threat-sensitive hypothesis and asset protection principle and highlight the phenotype-dependency and adaptive plasticity of alternative mating tactics in male guppies under varying predation risk.

opencc-zeroOct 2023View details →

opencc-by-4.0Feb 2023View details →

Predicting Methane Formation Rates of Freshwater Sediments in Different Biogeographic Regions

These datasets uploaded here contain: CH4 formation rates of sediment layers incubated from nine Swedish lakes (<a href="https://zenodo.org/api/files/39e74df0-e0c7-41a6-88c6-d37bcc1ffb49/CH4Formation_SwedishLakes.csv">CH4Formation_SwedishLakes.csv</a>),  their sediment characteristics measured in different layers and land use data CH4 formation rates retrieved from Isidorova et al. (2019,<a href="http://urn.kb.se/resolve?urn=urn%3Anbn%3Ase%3Auu%3Adiva-387547">urn:nbn:se:uu:diva-387547</a>). From this dataset we extracted only the maximum CH4 formation rates and are reported in the file <a href="https://zenodo.org/api/files/39e74df0-e0c7-41a6-88c6-d37bcc1ffb49/CH4Formation_BrazilianReservoirs.csv">CH4Formation_BrazilianReservoirs.csv</a> CH4 formation rates used to calculate CH4 formation decay rate with age for Swedish lakes (<a href="https://zenodo.org/api/files/39e74df0-e0c7-41a6-88c6-d37bcc1ffb49/CH4Formation_SwedishLakes_forDecayRate.csv">CH4Formation_SwedishLakes_forDecayRate.csv</a>) Supplementary information <a href="https://zenodo.org/api/files/4a6d58c8-6f6b-4665-93d2-14554fc8eb0e/SupplementaryInfo.pdf">SupplementaryInfo.pdf</a> related to CH4 formation rates and sediment age Extraction of PLS analysis results <a href="https://zenodo.org/api/files/39e74df0-e0c7-41a6-88c6-d37bcc1ffb49/PLS_List.xlsx">PLS_List.xlsx</a>

Prediction of Port Recovery Time after a Severe Storm Project

Predicting the impact of incoming tropical cyclones on ports in terms of the number of days underperforming is crucial for the effective management of the ports. However, existing methods perform undesirably due to the limited data and the inherent uncertainty associated with cyclone trajectory forecasting. This study applies a recommendation algorithm to address these challenges by focusing on predicting port impact rankings instead of predicting the duration of port impacts, which is often inaccurate and unreliable. First, we have collected comprehensive features of ports and hurricanes in the Gulf of Mexican and employed a modular time-series regression model to determine the duration of port impacts due to tropical cyclones, leveraging vessel count data extracted from the Automatic Identification System (AIS). Inspired by the recommendation algorithm, we recast tropical cyclones and ports as “user” and “items,” respectively, while the duration of port impacts represents their “interaction,” offering an innovative approach to model and analyze cyclone effects on ports. The Factorization Machine (FM) is adopted to learn the relationship between features (i.e., ports and cyclones) and subsequently conduct the port impact ranking. Finally, utilizing the hurricanes Alex, Ian, and Nicole that happened in 2022 as testing cases, the FM-based model excels in prediction performance and robustness against uncertainties compared to the widely used distance-based method. This study aims to provide port authorities and other stakeholders with a trustworthy tool for informed disaster management decisions, thereby enhancing port resilience.

Prediction of the time-domain chirp response at El Castillo

WAV file of the simulated time-domain chirp response of the El Castillo pyramid to an impulsive point excitation, using a ray model and a numerically computed trapped mode dispersion curve.

opencc-zeroOct 2023View details →

dryad36/100

Dispersal limitation predicts the spatial and temporal filtering of tropical bird communities in isolated forest fragments

The link between dispersal traits and patterns of community assembly remains a frontier in understanding how vertebrate communities persist in fragmented landscapes. Using experimental release trials and intensive field surveys of bird communities in fragmented forests of the Peruvian and Colombian Andes, we demonstrate that morphological traits related to movement (1) predict experimental flight performance and (2) exhibit dispersal-mediated environmental filtering at the community scale. After correcting for body size, four traits hypothesized to influence flight ability (wing length, wing pointedness, wing loading, and eye size) predicted distance flown across a hostile experimental landscape, with successful species having significantly longer pointed wings, carrying less mass per unit wing area (i.e., lower wing loading), and having smaller eyes. Species with larger eyes also displayed increased flight latency, potentially due to disability glare. At the community scale we detected a gradient of dispersal-mediated environmental filtering in fragments compared to reference forest within the same landscape, with relative differences in trait values explained by the temporal and spatial extent of patch isolation. In the Colombian landscape where fragments had been isolated for > 60 years, communities were filtered for species with long and narrow wings and small eyes, especially within the most spatially isolated fragments. We observed the opposite pattern in the more recently fragmented Peruvian landscape (15-30 years): communities within fragments tended to have shorter and more rounded wings compared to those in nearby contiguous forests, suggesting that dispersal-limited species accumulate in the initial years following patch isolation due to "restricted dispersal" and represent an extinction debt yet to be paid. Our results (1) experimentally validate the use of morphological traits as proxies for movement ability in fragmented landscapes, (2) demonstrate that visual acuity functions as a novel dimension of dispersal limitation, and (3) quantify how the spatial and temporal components of patch isolation produce a gradient in dispersal-mediated environmental filtering and extinction debt for communities inhabiting fragments.

opencc-by-nc-nd-4.0Oct 2023View details →

Supplementary Data for "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation"

Supplementary data for publication "Using AlphaFold and Experimental Structures for the Prediction of the Structure and Binding Affinities of GPCR Complexes via Induced Fit Docking and Free Energy Perturbation".Includes:<ul><li>All input structures used in the the retrospective benchmark dataset as well as the (at most) 5 best scoring output models.</li><li>Input structures and output models for IFD-MD predictions of SSTR2, SSTR4, and SSTR5 complexes.</li><li>Output FEP+ maps (in fmp format) for SSTR2, SSTR4, and SSTR5 best models (representative runs shown in publication).</li></ul>

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

ESCOTT mutational effect predictions for ProteinGym Substitutions Dataset

ESCOTT mutational effect predictions for 87 substitution experiments in the ProteinGym Dataset.

Which Parameters of Short-term Blood Pressure Variability Best Predict Early Outcomes in Acute Ischemic Stroke

ClinicalTrials.gov study NCT02675972. IPD Sharing: NO. Countries: 1. Publications: 1.

restrictedIPD-UNDECIDEDFeb 2026View details →

[18F]FLT-PET as a Predictive Imaging Biomaker of Treatment Responses to Regorafenib

ClinicalTrials.gov study NCT02175095. IPD Sharing: Not stated. Countries: 1. Publications: 27.

Inflammatory Indices in Predicting the Failure of Inhaled Corticosteroids Reduction in Young Participants With Asthma

ClinicalTrials.gov study NCT03788057. IPD Sharing: NO. Countries: 1. Publications: 11.

Ultrasound Elastography to Predict Development of SOS

ClinicalTrials.gov study NCT03858530. IPD Sharing: NO. Countries: 1. Publications: 23.

Advanced Prediction of Respiratory Depression Episode Using the Linshom Continuous Predictive Respiratory Sensor

ClinicalTrials.gov study NCT06744790. IPD Sharing: NO. Countries: 1. Publications: 11.

restrictedIPD-UNDECIDEDFeb 2026View details →

Liposomal Doxorubicin, Bevacizumab, and Everolimus in Patients With Locally Advanced TNBC With Tumors Predicted Insensitive to Standard Chemotherapy; A Moonshot Initiative

ClinicalTrials.gov study NCT02456857. IPD Sharing: Not stated. Countries: 1. Publications: 1.