Find research datasets worth reusing

opencc-by-4.0Jun 2022View details →

VGQ-CNN: Moving Beyond Fixed Cameras and Top-Grasps for Grasp Quality Prediction

This dataset includes all the data and trained models to replicate our work for VGQ-CNN (accepted for IJCNN 2022). You can find the code to use this dataset on <a href="https://github.com/AuCoRoboticsMU/vgq-cnn">github</a>. To replicate the work done for VGQ-CNN, use the data in vgq-dset.zip. Trained models of VGQ-CNN, Fast-VGQ-CNN and GQ-CNN are available in VGQ-CNN_models.zip.   To create your own, subsampled training and testing data, adjust our code on github to your subsampling constraints and use full_rendered_dset (created by unpacking full_rendered_dset_tensors.zip and full_rendered_dset_images.zip into the unpacked directory of full_rendered_dset_info.zip).

opencc-zeroJun 2022View details →

Visual complexity of egg patterns predicts egg rejection according to Weber's law

Visual complexity is ubiquitous in nature. Drivers of complexity include selection in coevolutionary arms races between antagonists. However, the causes and consequences of biological complexity and its perception are largely understudied, partly because complexity is difficult to quantify. Here, we address this by studying egg pattern complexity and its perception in hosts (tawny-flanked prinia Prinia subflava ), which visually recognize and reject mimetic eggs of their virulent brood parasite (cuckoo finch Anomalospiza imberbis ). Using field data and an optimization algorithm, we compute a complexity metric which predicts rejection of experimentally placed conspecific eggs in prinia nests. Real cuckoo finch eggs exhibit significantly lower pattern complexity than prinia eggs, suggesting that high complexity benefits hosts because it distinguishes host eggs from parasitic eggs. We show that prinias perceive complexity differences according to Weber's law of proportional processing (i.e. relative, rather than absolute, differences between stimuli are processed in discrimination, such that two eggs with simple patterns are more easily discriminable than two with complex patterns). This may influence coevolutionary trajectories of hosts and parasites. The new methods presented for quantifying complexity and its perception can help us to understand selection pressures driving the evolution of complexity and its consequences for species interactions.

opencc-zeroJun 2022View details →

Data from: Floral evolution and pollinator diversification in Hedychium: revisiting Darwin's predictions using an integrative taxonomic approach

Hedychium J.Koenig (Zingiberaceae) is endemic to the Indo-Malayan Realm and is known for its vibrant and fragrant flowers. Historically, two different pollination syndromes characterize Hedychium: diurnal or bird pollination and nocturnal or moth pollination. In this study we aim to understand the evolution of nocturnal and diurnal flowers, and to test its putative association with lineage diversification in Hedychium. A molecular tree of Hedychium was used as a scaffold upon which we estimated ancestral character-states, phylogenetic signals, and correlations for certain categorical and continuous floral traits. Further, we employed phylomorphospace and trait-dependent diversification rate estimation analyses to understand phenotypic evolution and associated lineage diversification in Hedychium. Although floral color and size lacked any association with specific pollinators, white or pale flowers were most common in the early branching clades, when compared to bright-colored flowers which were more widely represented in the most derived clade IV. Five categorical and two continuous characters were identified to have informative evolutionary patterns which also emphasized that ecology may have played a critical role in the diversification of Hedychium. From our phylogenetic analyses and ecological observations, we conclude that specializations in pollinator interactions are rare in the hyperdiverse clade IV, thus challenging the role of both moth-specialization and bird-specialization as central factors in the diversification of Hedychium. However, our results also suggest that clade III (predominantly island clade) may show specializations, and future studies should investigate ecological and pollinator interactions, along with inclusion of new traits such as floral fragrance and anthesis time.

opencc-zeroJun 2022View details →

eDNAssay: a machine learning tool that accurately predicts qPCR cross-amplification

Environmental DNA (eDNA) sampling is a highly sensitive and cost-effective technique for wildlife monitoring, notably through the use of qPCR assays. However, it can be difficult to ensure assay specificity when many closely related species cooccur. In theory, specificity may be assessed in silico by determining whether assay oligonucleotides have enough base-pair mismatches with nontarget sequences to preclude amplification. However, the mismatch qualities required are poorly understood, making in silico assessments difficult and often necessitating extensive in vitro testing—typically the greatest bottleneck in assay development. Increasing the accuracy of in silico assessments would therefore streamline the assay development process. In this study, we paired 10 qPCR assays with 82 synthetic gene fragments for 530 specificity tests using SYBR Green intercalating dye (n = 262) and TaqMan hydrolysis probes (n = 268). Test results were used to train random forest classifiers to predict amplification. The primer-only model (SYBR Green-based) and full-assay model (TaqMan probe-based) were 99.6% and 100% accurate, respectively, in cross-validation. We further assessed model performance using six independent assays not used in model training. In these tests the primer-only model was 92.4% accurate (n = 119) and the full-assay model was 96.5% accurate (n = 144). The high performance achieved by these models makes it possible for eDNA practitioners to more quickly and confidently develop assays specific to the intended target. Practitioners can access the full-assay model via eDNAssay (https://NationalGenomicsCenter.shinyapps.io/eDNAssay), a user-friendly online tool for predicting qPCR cross-amplification.

opencc-zeroMar 2023View details →

How to quantify factors degrading DNA in the environment and predict degradation for effective sampling design

Extra-organismal DNA (eoDNA) from material left behind by organisms (non-invasive DNA: e.g., faeces, hair) or from environmental samples (eDNA: e.g., water, soil) is a valuable source of genetic information. However, the relatively low quality and quantity of eoDNA, which can be further degraded by environmental factors, results in reduced amplification and sequencing success. This is often compensated for through cost- and time-intensive replications of genotyping/sequencing procedures. Therefore, system- and site-specific quantifications of environmental degradation are needed to maximize sampling efficiency (e.g., fewer replicates, shorter sampling durations), and to improve species detection and abundance estimates. Using ten environmentally diverse bat roosts as a case study, we developed a robust modelling pipeline to quantify the environmental factors degrading eoDNA, predict eoDNA quality, and estimate sampling-site-specific ideal exposure duration. Maximum humidity was the strongest eoDNA-degrading factor, followed by exposure duration and then maximum temperature. We also found a positive effect when hottest days occurred later. The strength of this effect fell between the strength of the effects of exposure duration and maximum temperature. With those predictors and information on sampling period (before or after offspring were born), we reliably predicted mean eoDNA quality per sampling visit at new sites with a mean squared error of 0.0349. Site-specific simulations revealed that reducing exposure duration to 2-8 days could substantially improve eoDNA quality for future sampling. Our pipeline identified high humidity and temperature as strong drivers of eoDNA degradation even in the absence of rain and direct sunlight. Furthermore, we outline the pipeline's utility for other systems and study goals, such as estimating sample age, improving eDNA-based species detection, and increasing the accuracy of abundance estimates.

opencc-zeroJul 2022View details →

An experimental test of the Growth Rate Hypothesis as a predictive framework for microevolutionary adaptation

The growth rate hypothesis (GRH), a central concept of ecological stoichiometry, posits that the relative body phosphorus content of an organism is positively related to somatic growth rate as protein synthesis, which is necessary for growth, requires P-rich rRNA and has strong support at the interspecific level. Here, we explore the use of the GRH to predict microevolutionary responses in consumer body stoichiometry. For this, we subjected zooplankton populations to selection for fast population growth (PGR) in P-rich (HPF) and P-poor (LPF) food environments. With common garden transplant experiments, we demonstrate that in HP populations evolution towards increased PGR was concomitant with an increase in relative phosphorus content. In contrast, LP populations evolved higher PGR without an increase in relative phosphorus content. We conclude that the GRH has the potential to predict microevolutionary change, but that its application is contingent on the environmental context. Our results highlight the potential of cryptic evolution in determining the performance response of populations to elemental limitation of their food resources.

opencc-zeroDec 2021View details →

Predicting potential distributions of large carnivores in Kenya: An occupancy study to guide conservation

Aim: Species distribution maps are frequently the foundation upon which species-specific conservation strategies are developed, however, mapping species distribution is challenging, especially across large spatial extents. Our aim was to use a novel empirical approach to predict the national distribution for all six large carnivore species found in Kenya to guide conservation and management decisions by identifying knowledge and conservation gaps. Location: Kenya Methods: Data on carnivore presence and absence were collected through questionnaires and sightings-based surveys. These data were combined and analysed using single-season false-positive occupancy models, which account for imperfect detections and false positives. To inform conservation strategies, we used the occupancy outputs to make predictions for unsampled areas and create occupancy-based distribution maps, where ψ>0.50, to (1) quantify differences with IUCN Red List range maps, (2) quantify overlap with wildlife areas and (3) identify areas of high carnivore richness. Results: Large carnivore occupancy was associated with land conversion, habitat, and prey availability. Our results suggest that all six species are widely distributed across Kenya and reveal substantial differences in distribution maps compiled by the IUCN Red List. More specifically, our occupancy-based distribution maps predict a much larger distribution for African wild dog (5.09X), lion (4.77X), and leopard (1.46X), similar distribution for cheetah, and smaller distribution for spotted hyaena (0.84X) and striped hyaena (0.65X). For all large carnivores, the vast majority (~80%) of their predicted distribution falls outside wildlife areas and northern Kenya is predicted to have the highest large carnivore richness. Main conclusions: Our results are encouraging as large carnivores may be widely distributed across Kenya, in some cases potentially more so than previously acknowledged. However, much of this range lies outside wildlife areas and represents areas of concern both for conservation and human livelihoods illustrating the challenges of conserving large carnivores across their range.

Prediction and Visualization of Human Transmembrane Proteins using AlphaFold and Protein Language Models

Description: TMvis ("TMvis496.tar.gz") is a dataset containing 496 3D-structures of predicted human transmembrane proteins (TMP) and their predicted membrane embedding. The method TMbed [1], based on the protein language model ProtT5 [2] predicted 4.967 TMP for the human proteome (20,375 proteins, UniProt [3] version April 2022; excluding TITIN_HUMAN due to length). For these proteins, we obtained AlphaFold [4] structures from AlphaFoldDB [5] with an average per-residue confidence score (pLDDT) of more than 90%. This resulted in the 496 proteins of TMvis, as can be found in "TMvis496.fasta". The membrane embedding was predicted using the methods ANVIL [6], PPM3 [7], and per-residue TMbed predictions. As the three methods are based on different approaches, we decided to publish results for all. The figure “TMvis_project_overview.png” provides a graphical overview for each step described above. TMvis Folder Structure: TMvis is separated into “alpha” containing predicted alpha-helical TMPs, and “beta” containing predicted beta-barrel TMPs. Within these folders, each protein is assigned one folder, identifiable by the respective unique UniProt ID. Each protein folder consists of: - “UniprotID.fasta” with UniProt ID, sequence, TMbed per-residue prediction - “AF-UniprotID-F1-model_v2.pdb” with the AlphaFold structure - “AF-UniprotID-F1-model_v2.cif” with the AlphaFold structure - “AF-UniprotID-F1-model_v2_ANVIL.pdb” with predicted ANVIL membrane embedding - “AF-UniprotID-F1-model_v2_ppm.pdb” predicted PPM3 membrane embedding TMvis                            |                                     ├── alpha                                  │   │                                  │   ├── A0A087X1C5                                  │   │   ├── A0A087X1C5.fasta                                  │   │   ├── AF-A0A087X1C5-F1-model_v2.pdb                                  │   │   ├── AF-A0A087X1C5-F1-model_v2.cif                                  │   │   ├── AF-A0A087X1C5-F1-model_v2_ANVIL.pdb                                  │   │   └── AF-A0A087X1C5-F1-model_v2_ppm.PDB                                  │   └── ...                                     └── beta                                      └── P45880 TMvis visualization: The 3D-visualization of every protein in the dataset TMvis can be easily accessed using the Jupyter Notebook “TMvis.ipynb”. It contains detailed descriptions the different membrane prediction tools ANVIL, PPM3, and TMbed as well as the respective code. Additionally, it allows to visualize the per-residue confidence scores (pLDDT) of AlphaFold. —————————————————————————————————————————————————————————————————————————— References: [1] TMbed - TMbed Bernhofer, Michael, and Burkhard Rost. 2022. “TMbed – Transmembrane Proteins Predicted through Language Model Embeddings.” bioRxiv. [2] ProtT5 - A. Elnaggar et al., "ProtTrans: Towards Cracking the Language of Lifes Code Through Self-Supervised Deep Learning and High Performance Computing," in IEEE Transactions on Pattern Analysis and Machine Intelligence, doi: 10.1109/TPAMI.2021.3095381. [3] UniProt - UniProt Consortium (2021). UniProt: the universal protein knowledgebase in 2021. Nucleic acids research, 49(D1), D480–D489. [4] AlphaFold - AlphaFold Jumper, John, Richard Evans, Alexander Pritzel, Tim Green, Michael Figurnov, Olaf Ronneberger, Kathryn Tunyasuvunakool, et al. 2021. “Highly Accurate Protein Structure Prediction with AlphaFold.” Nature 596 (7873): 583–89. [5] Alphafold DB - Varadi, Mihaly, Stephen Anyango, Mandar Deshpande, Sreenath Nair, Cindy Natassia, Galabina Yordanova, David Yuan, et al. 2022. “AlphaFold Protein Structure Database: Massively Expanding the Structural Coverage of Protein-Sequence Space with High-Accuracy Models.” Nucleic Acids Research 50 (D1): D439–44. [6] ANVIL - ANVIL Postic, Guillaume, Yassine Ghouzam, Vincent Guiraud, and Jean-Christophe Gelly. 2016. “Membrane Positioning for High- and Low-Resolution Protein Structures through a Binary Classification Approach.” Protein Engineering, Design & Selection: PEDS 29 (3): 87–91. [7] PPM3 - PPM3 Lomize, Mikhail A., Irina D. Pogozheva, Hyeon Joo, Henry I. Mosberg, and Andrei L. Lomize. 2012. “OPM Database and PPM Web Server: Resources for Positioning of Proteins in Membranes.” Nucleic Acids Research 40 (Database issue): D370–76. —————————————————————————————————————————————————————————————————————————— License: This work is licensed under a Creative Commons Attribution 4.0 International License (CC-BY 4.0).

opencc-by-4.0Jun 2022View details →

Code and data for "Global warming generates predictable extinctions of warm- and cold-water marine benthic invertebrates via thermal habitat loss"

<pre>This repository contains the following information: Datasets S1 to S4 can all be loaded, manipulated, and analysed in R using script provided in Data S5 to obtain the results of the paper, Reddin et al. 2022, "Global warming generates predictable extinctions of warm and cold-water marine benthic invertebrates via thermal habitat loss". Data S1. (separate file) The original downloaded PaleoDB dataset. Data S2. (separate file) The pre-prepared dataset of occurrences. Data S3. (separate file) The finished environmental dataset. Data S4. (separate file) Additional environmental dataset. Data S5. (separate file) The R-code for the main analysis. Data S6. (compressed directory) Output data and code from the simulations. Table S7 (separate file). List of data source publications for PaleoDB data used in our study. Listed are the data source author list (ref_author), year (ref_pubyr), and reference number as appears in the PaleoDB (reference_no). </pre>

Microclimate simulation output: "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens"

The following microclimate simulation dataset supports the paper "Between vision and action: the predicted effects of co-designed green infrastructure solutions on environmental burdens" by Mathias Schaefer, published in Urban Ecosystems (2022). "T0Simulation_11082020_output" contains data about the status quo simulation of the area of interest (500 m x 500 m x 60 m), whereas "T1Simulation_11082020_output" shows the results of the Green Infrastructure scenario described in the research article above. Please ensure enough memory space on your device, as both files have a size of approximately 25 GB (unzipped). The output files can be visualized with the ENVI-met Leonardo extension. The ENVI-met LITE-version is freely available and can be downloaded at the <a href="https://envi-met.info/doku.php?id=files:download">ENVI-met homepage</a>. Alternatively, the included .NETCDF files can be imported as a multidimensional raster dataset in ArcGIS Pro. Files in the folder "atmosphere" represent meteorological parameters such as potential air temperature [°C], relative humidity [%], or wind speed [m/s]. Air pollution calculations like particulate matter concentrations [µg/m³] can be found in the folder "pollutants". The folder "buildings" contains building data for 3D visualizations of surface temperatures [°C].

Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning

This is the source code and data for the publication "Predicting Shallow Water Dynamics using Echo-State Networks with Transfer Learning". Preprint - https://arxiv.org/abs/2112.09182

Data used in a manuscript entitled "Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model" submitted to Geophysical Research Letters

This include a dataset used in a manuscript entitled “Large ensemble simulation for investigating predictability of precursor vortices of Typhoon Faxai in 2019 with a 14-km mesh global nonhydrostatic atmospheric model” by Yamada and co-authors, which is submitted to Geophysical Research Letters. Contact: Yohei Yamada (yoheiy@jamstec.go.jp)

opencc-by-4.0Jun 2022View details →

Data, scripts, and figures of the article: Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models

This data set contains the data, JMP scripts, and figures of the article titled "Processing weights of chickens determined by Dual-Energy X-Ray Absorptiometry. 3. Validation of prediction models" to be published in the journal Animal - Open Space.

Observed and Predicted Bulk Sediment Uranium Isotope Composition (δ234U) for ODP 1094 Over the Last 500 kyr

This is supplementary data associated with a manuscript entitled "Anomalous 234U/238U Isotopic Composition in Southern Ocean Sediments" by N. Redmond, C. T. Hayes, S. K. Glasscock, E. Rohde, R. F. Anderson, and D. McGee Variables Sample ID - Section of ODP 1094 core. Hole-Core-Section-Depth(cms) MCD_TOP - Meters Composite Depth at the top of the section Age (Hasenfratz et al., 19) - in kyrs, based on tuning of benthic δ18O to LR04 stack U-238 - Concentration of 238U in bulk sediment, units in ppm or micrograms/gram. aU - Authigenic uranium concentration, calculated from removing th-based detrital U fraction from bulk 238U, units in ppm or micrograms/gram δ234U Observed - Uranium isotope composition observed in bulk sediment, written as the per mil deviation from secular equilibrium, units in ppm or micrograms/gram δ234U Observed Error - Uncertainty Uranium isotope composition observed in bulk sediment, written as the per mil deviation from secular equilibrium, units in ppm or micrograms/gram δ234U Modeled - Uranium isotope composition predicted in bulk sediment by a mixing model of authigenic and detrital U, decayed with age to match observed data, written as the per mil deviation from secular equilibrium, units in ppm or micrograms/gram δ234U Modeled Error - Uncertainty of Uranium isotope composition predicted in bulk sediment by a mixing model of authigenic and detrital U, decayed with age to match observed data, written as the per mil deviation from secular equilibrium, units in ppm or micrograms/gram

Resource-Rational Lossy-Context Surprisal (Model Predictions)

Resource-Rational Lossy-Context Surprisal is a computationally implemented model of how humans process language, predicting at what points in complex sentences they experience comprehension difficulty. It unifies the memory-based and expectation-based paradigms in psycholinguistics, and provides a more refined account of when hierarchical structure is difficult to comprehend for humans. This repository contains output of the model on a battery of test sentences exhibiting iterated recursive structure, described in associated publications on Resource-Rational Lossy-Context Surprisal. The filenames are referred to in the source code, to be published together with a forthcoming journal publication on the model. The model was first described in the following publication: Lexical Effects in Structural Forgetting: Evidence for Experience-Based Accounts and a Neural Network Model (Michael Hahn, Richard Futrell, Edward Gibson), 33rd Annual CUNY Human Sentence Processing Conference, 2020

Fibrinogen depletion coagulopathy predicts major bleeding after thrombolysis for ischemic stroke: a multicentre study

Dataset from "Fibrinogen depletion coagulopathy predicts major bleeding after thrombolysis for ischemic stroke: a multicentre study", Romoli & Vandelli et al., Stroke 2022

opencc-by-4.0Apr 2022View details →

Raw Data to "Prediction of acid pKa values in the solvent acetone based on COSMO-RS"

This data is a supplement to the publication entitled "Prediction of Acid pKa Values in the Solvent Acetone based on COSMO-RS" in the Journal of Computational Chemistry (DOI:10.1002/jcc.26864). The data includes initial starting structures as inputs for conformer searches using COSMOconf (versions 2020 and 2021) in combination with TURBOMOLE (version 7.3). The corresponding output files serve as inputs for the calculation of Gibbs free energies using COSMO-RS as provided by COSMOtherm. Additional information on the file structure is given in the README file.

Predicting Patch Correctness Based on the Similarity of Failing Test Cases

Dataset for the paper "Predicting Patch Correctness Based on the Similarity of Failing Test Cases" Tool name: BATS

Codes for Purgar et al. 2022: Investigating the ability of growth models to predict in situ Vibrio spp. abundances

Model simulations and analysis of the Vibrio spp. growth models.  Prepared to accompany the publication, Purgar et al. 2022 "Investigating the ability of growth models to predict in situ Vibrio spp. abundances" in Microorganisms, Special Issue &bdquo;Microbial Communities in Changing Aquatic Environments“.  Description of the files can be found in the Readme file.