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13,064 results for “predictions”
Supplementary data and code: Human–Wildlife Interactions Predict Febrile Illness in Park Landscapes of Western Uganda
<p>Data at code used for analyses.</p> <p>Salerno J, Ross N, Ghai R, et al (2017) Human–Wildlife Interactions Predict Febrile Illness in Park Landscapes of Western Uganda. EcoHealth. doi: 10.1007/s10393-017-1286-1<br> </p>
Data from: Prediction of Pedestrian Speed with Artificial Neural Networks
<p>Corridor data are trajectories of pedestrians in a closed corridor of lenght 30m and width 1.8m. The trajectories are measured on a section of length 6m. Experiments are carried out with N=15, 30, 60, 85, 95, 110, 140 and 230 participants.</p> <p>Bottleneck data are trajectories of pedestrian in a bottleneck of lenght 8m and width 1.8m. Experiments are carried out with 150 participants for bottleneck widths w=0.7, 0.95 1.2 and 1.8m.</p> <p>See http://ped.fz-juelich.de/experiments/2009.05.12_Duesseldorf_Messe_Hermes/docu/VersuchsdokumentationHERMES.pdf page 20 and 24 for details (in German). The data are part of the online database http://ped.fz-juelich.de/database.</p> <p>Column names of the file are: ID FRAME X Y Z.</p> <ul> <li>ID is the pedestrian ID.</li> <li>FRAME is the frame number (frame rate is 1/16s).</li> <li>X Y and Z are pedestrian position in 3D.</li> </ul>
Data and script for Andrews et al. 'A marker of biological ageing predicts risk preference in European starlings, Sturnus vulgaris'
<p>Data and script for Andrews et al. 'A marker of biological ageing predicts risk preference in European starlings, Sturnus vulgaris'. Consists: One R script and three data .csv files.</p>
Figure 1 Scatter Plot of Standardized Residual by Standardized Predicted Value-Potential Predictability of ZPD of Children's Cognitive Development
<p>One of the essential assumptions that should be met in regression analysis is the linearity of<br> the data. The result of the analysis of variance (ANOVA) shows that regression model is linear; F<br> (1, 39) = 8.429, P <0.05 for model 1 and F (2, 38) = 8.648, P<0.05 for model 2. Moreover, Scatter<br> plot shows (Figure1) that there is no funnel shaped or crescent shaped cloud. This indicates that two<br> assumptions of linearity and homogeneity of variance have been met.</p>
Results and code associated with «Predictability of ecological and evolutionary dynamics in a changing world»
<p>Below you'll find results and code associated with the following article (*):</p> <p>Bozzuto, C, Ives, AR (2024): Predictability of ecological and evolutionary dynamics in a changing world. <em>Proceedings of the Royal Society B</em>, <strong>291</strong>: 20240980. https://doi.org/10.1098/rspb.2024.0980</p> <p>ABSTRACT: Ecological and evolutionary predictions are being increasingly employed to inform decision-makers confronted with intensifying pressures on biodiversity. For these efforts to effectively guide conservation actions, knowing the limit of predictability is pivotal. In this study, we provide realistic expectations for the enterprise of predicting changes in ecological and evolutionary observations through time. We begin with an intuitive explanation of predictability (the extent to which predictions are possible) employing an easy-to-use metric, predictive power <em>PP</em>(<em>t</em>). To illustrate the challenge of forecasting, we then show that among insects, birds, fishes and mammals, (i) 50% of the populations are predictable at most 1 year in advance and (ii) the median 1-year-ahead predictive power corresponds to a prediction <em>R</em><sup>2</sup> of only 20%. Predictability is not an immutable property of ecological systems. For example, different harvesting strategies can impact the predictability of exploited populations to varying degrees. Moreover, incorporating explanatory variables, accounting for time trends and considering multivariate time series can enhance predictability. To effectively address the challenge of biodiversity loss, researchers and practitioners must be aware of the information within the available data that can be used for prediction and explore efficient ways to leverage this knowledge for environmental stewardship.</p> <p>(*) previously a preprint on <em>bioRxiv</em>: https://doi.org/10.1101/2023.11.01.565089</p>
Photometric Galaxy Redshift Prediction
<h3>Sloan Digital Sky Survey (SDSS) Galaxy Redshift Dataset</h3> <p>This dataset comprises a curated collection of galaxy observations from the Sloan Digital Sky Survey (SDSS). It features photometric and spectroscopic data for 100 galaxies, specifically selected to cover a range of redshifts from 0 to 0.4. The dataset includes the following key parameters for each galaxy:</p> <ul> <li><strong>Photometric Data</strong>: Magnitudes in the SDSS 'u', 'g', 'r', 'i', and 'z' bands.</li> <li><strong>Spectroscopic Data</strong>: Measured redshift (<code>redshift</code>) and its error (<code>redshift_error</code>).</li> <li><strong>Additional Metadata</strong>: <ul> <li><code>objid</code>: Unique identifier for the photometric object.</li> <li><code>specObjID</code>: Unique identifier for the spectroscopic object.</li> <li><code>ra</code>: Right ascension in decimal degrees.</li> <li><code>dec</code>: Declination in decimal degrees.</li> <li><code>class</code>: Classification of the object, all marked as 'GALAXY'.</li> </ul> </li> </ul> <h3>Purpose and Use</h3> <p>This dataset is intended for use in astronomical research and education, particularly in studies involving galaxy properties and distribution, cosmology, and machine learning applications such as redshift prediction models. The data is well-suited for developing and testing predictive models that estimate redshifts from photometric data, aiding in the expansion of accessible astronomical analysis tools.</p> <h3>Data Collection Method</h3> <p>The data was extracted using SQL queries against the public SDSS DR16 database, ensuring accuracy and relevance in current astronomical research contexts.</p> <h3>Accessibility</h3> <p>The dataset is made available under a CC0 license to promote open scientific research and collaboration within the astronomical community and beyond.</p>
Comparative ecological analysis and predictive modeling of tick-borne pathogens
<p>Tick-borne diseases constitute the predominant vector-borne health threat in North America. Recent observations have noted a significant expansion in the range of the black-legged tick (<em>Ixodes scapularis</em> Say, Acari: Ixodidae), alongside a rise in the incidence of diseases caused by its vectored pathogens: <em>Borrelia burgdorferi</em> (Spirochaetales: Spirochaetaceae), <em>Babesia microti</em> (Piroplasmida: Babesiidae), and <em>Anaplasma phagocytophilium</em> (Rickettsiales: Anaplasmataceae), the causative agents of Lyme disease, babesiosis, and anaplasmosis, respectively. Prior research identified environmental features that influence the ecological dynamics of <em>I. scapularis</em> and <em>B. burgdorferi</em> that can be used to predict the distribution and abundance of these organisms, and thus Lyme disease risk. In contrast, there is a paucity of research into the environmental determinants of <em>B. microti</em> and <em>A. phagocytophilium</em>. Here we use over a decade of surveillance data to model the impact of environmental features on the infection prevalence of these increasingly common human pathogens in ticks across New York State (NYS). Our findings reveal a consistent northward and westward expansion of <em>B. microti</em> in NYS from 2009 to 2019, while the range of <em>A. phagocytophilum</em> varied at fine spatial scales. We constructed biogeographic models using data from over 1000 site-year visits and encompassing more than 250 environmental variables to accurately forecast infection prevalence for each pathogen to future years that were not included in model training. Several environmental features were identified to have divergent effects on the pathogens, revealing potential ecological differences governing their distribution and abundance. These validated biogeographic models are immediately useful for disease prevention efforts.</p>
Data for 'VespaG: Expert-guided protein language models enable accurate and blazingly fast fitness prediction'
<div>Datasets used for development of VespaG and VespaG predictions generated with <a href="https://github.com/JSchlensok/VespaG">https://github.com/JSchlensok/VespaG</a>. </div> <div> </div> <div>Uploads contain:</div> <div> <ol> <li><strong>Performance</strong> summaries for ProteinGym [1]:<br>- Spearman and Pearson correlation for VespaG: <em>proteingym_performance_vespag.csv </em>(columns: 'DMS_id', 'Spearman', 'Pearson')<br>- Spearman correlation for evaluated methods VespaG, GEMME [2], VESPA [3], TranceptEVE [4], AlphaMissense [5], PoET [6]: <em>proteingym_spearman_allmethods.csv </em>(columns: 'DMS_id', 'Trancept EVE-L', 'VESPA', 'VespaG', 'GEMME', 'AlphaMissense', 'PoET', 'UniProt_ID', 'coarse_selection_type' (function), 'taxon')</li> <li><strong>Fasta</strong> files with sequences for all train sets (<em>vespag_fasta_training_datasets.zip</em> with seq_all9k.fasta, seq_human5k.fasta, seq_droso4k.fasta, seq_ecoli2k.fasta, seq_virus1k.fasta) and test set (<em>proteingym_217.fasta</em>)</li> <li><strong>VespaG</strong> <strong>Predictions</strong> for test set: <em>vespag_proteingym_rawpreds_by_training_dataset.zip</em> with raw_preds_ecoli.csv, raw_preds_human.csv, raw_preds_virus.csv, raw_preds_all.csv, raw_preds_droso.csv (columns: 'DMS_id', 'mutation', 'DMS_score', 'VespaG'). Predictions are based on different training data, the final model VespaG was trained on a subset of the human proteome and <strong>raw VespaG predictions</strong> <strong>for</strong> <strong>the</strong> <strong>ProteinGym benchmark are in raw_preds_human.csv </strong>(used to calculate the performances above).</li> <li><strong>GEMME predictions</strong> for train sets: <em>vespag_proteingym_rawpreds_by_training_dataset.zip </em>with folders 'human', 'droso', 'ecoli', 'virus', 'all' for respective fasta file (each containing GEMME mutational landscape output files named '<em>ID' + '</em>_normPred_evolCombi.txt')</li> <li><strong>ESM-2</strong> <strong>embeddings</strong> [7] for test set (<em>proteingym_217_esm2.h5</em>)</li> </ol> </div> <div>For details on VespaG see:</div> <div> <div> <div>VespaG: Expert-guided protein Language Models enable accurate and blazingly fast fitness prediction</div> </div> <div>Celine Marquet, Julius Schlensok, Marina Abakarova, Burkhard Rost, Elodie Laine</div> <div>bioRxiv 2024.04.24.590982; doi: https://doi.org/10.1101/2024.04.24.590982</div> <div> </div> <div>For more information on data usage and generation please see <a href="https://github.com/JSchlensok/VespaG">https://github.com/JSchlensok/VespaG</a>.</div> <div> </div> <div>Abstract:</div> <div>Exhaustive experimental annotation of the effect of all known protein variants remains daunting and expensive, stressing the need for scalable effect predictions. We introduce VespaG, a blazingly fast single amino acid variant effect predictor, leveraging embeddings of protein Language Models as input to a minimal deep learning model. To overcome the sparsity of experimental training data, we created a dataset of 39 million single amino acid variants from the human proteome applying the multiple sequence alignment-based effect predictor GEMME as a pseudo standard-of-truth. Assessed against the ProteinGym Substitution Benchmark (217 multiplex assays of variant effect with 2.5 million variants), VespaG achieved a mean Spearman correlation of 0.48 +/- 0.01, matching state-of-the-art methods such as GEMME, TranceptEVE, PoET, AlphaMissense, and VESPA. VespaG reached its top-level performance several orders of magnitude faster, predicting all mutational landscapes of the human proteome in 30 minutes on a consumer laptop (12-core CPU, 16 GB RAM).</div> <div> </div> <div>[1] Notin, Pascal, et al. "ProteinGym: large-scale benchmarks for protein fitness prediction and design." <em>Advances in Neural Information Processing Systems</em> 36 (2024).<br>[2] Laine, Elodie, Yasaman Karami, and Alessandra Carbone. "GEMME: a simple and fast global epistatic model predicting mutational effects." <em>Molecular biology and evolution</em> 36.11 (2019): 2604-2619.</div> <div>[3] Marquet, Céline, et al. "Embeddings from protein language models predict conservation and variant effects." <em>Human genetics</em> 141.10 (2022): 1629-1647.</div> <div>[4] Notin, Pascal, et al. "TranceptEVE: Combining family-specific and family-agnostic models of protein sequences for improved fitness prediction." <em>bioRxiv</em> (2022): 2022-12.</div> <div>[5] Cheng, Jun, et al. "Accurate proteome-wide missense variant effect prediction with AlphaMissense." <em>Science</em> 381.6664 (2023): eadg7492.</div> <div>[6] Truong Jr, Timothy, and Tristan Bepler. "PoET: A generative model of protein families as sequences-of-sequences." <em>Advances in Neural Information Processing Systems</em> 36 (2024).</div> <div>[7] Lin, Zeming, et al. "Evolutionary-scale prediction of atomic-level protein structure with a language model." <em>Science</em>379.6637 (2023): 1123-1130.</div> </div>
ARSO weather data for flood prediction
<p>This data was gathered and aggreagated from ARSO - SLOVENIAN ENVIRONMENTAL AGENCY - archive.</p> <p>Column info:</p> <ul> <li>station_id = id of the station used for weather data from ARSO</li> <li>padavine_klicina = amount of rain in mm</li> <li>temperatura_avg = average temperature in C</li> <li>veter_hitrost = wind speed in m/s</li> <li>veter_sunki = max wind speed in m/s</li> <li>month = month number</li> <li>vodostaj_cm = water level in cm</li> </ul> <p>Vir podatkov: Agencije Republike Slovenije za okolje (ali skrajšano: ARSO). ARSO ne prevzema nobene odgovornosti v povezavi z uporabo objavljenih podatkov ali za odločitve, sprejete na osnovi objavljenih podatkov.</p>
Dataset related to article: QUALITY ASSESSMENT OF THE MRI-RADIOMICS STUDIES FOR MGMT PROMOTER METHYLATION PREDICTION IN GLIOMA: A SYSTEMATIC REVIEW AND META-ANALYSIS
<p><strong><span>This table contains the raw data used to generate the heatmap illustrated in Fig 2. Each row of the table shows the distribution of the scores achieved by the studies for a domain. Colors from red to green denote progressive increase from minimum to maximum score obtainable for each domain.</span></strong></p>
[Accompanying Dataset for PHIStruct] ColabFold-Predicted Structures of Receptor-Binding Proteins
<p><strong>This dataset contains protein structures, computationally predicted via <a href="https://doi.org/10.1038/s41592-022-01488-1">ColabFold</a>, of 19,081 non-redundant (i.e., with duplicates removed) receptor-binding proteins from 8,525 phages across 238 host genera</strong>. We identified these receptor-binding proteins based on GenBank annotations. For phage sequences without GenBank annotations, we employed a pipeline that uses the viral protein library <a href="https://doi.org/10.1093/nargab/lqab067">PHROG</a> and the machine learning model <a href="https://doi.org/10.3390/v14061329">PhageRBPdetect</a>. </p> <p>More details can be found in our paper <strong>"PHIStruct: Improving phage-host interaction prediction at low sequence similarity settings using structure-aware protein embeddings."</strong> The project page is <a href="https://github.com/bioinfodlsu/PHIStruct">https://github.com/bioinfodlsu/PHIStruct</a>. Our paper is published in <em>Bioinformatics:</em> <a href="https://doi.org/10.1093/bioinformatics/btaf016" rel="nofollow">https://doi.org/10.1093/bioinformatics/btaf016</a></p> <p>Our research was supported with Cloud TPUs from <a href="https://sites.research.google/trc/about/" rel="nofollow">Google's TPU Research Cloud (TRC)</a> and with computing resources from the <a href="https://docs.mlerp.cloud.edu.au/" rel="nofollow">Machine Learning eResearch Platform (MLeRP)</a> of Monash University, University of Queensland, and Queensland Cyber Infrastructure Foundation Ltd.</p>
Baseline embeddings from the BBBC022 dataset used in "Semisupervised contrastive learning for bioactivity prediction using Cell Painting image data"
<p>3 Baseline embeddings aclculated from the BBBC022 dataset. A self-supervised contrastive learning-based model, DINO and CellProfiler were used to calculate the embeddings.</p>
Conformer datasets for "Equivariant Graph Neural Networks for Toxicity Prediction"
<p>Predictive modeling of toxicity is a crucial step in the drug discovery pipeline. It can help filter out molecules with a high probability of failing in the early stages of de novo drug design. Thus, several machine learning (ML) models have been developed to predict the toxicity of molecules by combining classical ML techniques or deep neural networks with well-known molecular representations such as fingerprints or 2D graphs. But the more natural, accurate representation of molecules is expected to be defined in physical 3D space like in ab initio methods. Recent studies successfully used equivariant graph neural networks (EGNNs) for representation learning based on 3D structures to predict quantum-mechanical properties of molecules. Inspired by this, we investigated the performance of EGNNs to construct reliable ML models for toxicity prediction. We used the equivariant transformer (ET) model in TorchMD-NET for this. Eleven toxicity data sets taken from MoleculeNet, TDCommons, and ToxBenchmark have been considered to evaluate the capability of ET for toxicity prediction. Our results show that ET adequately learns 3D representations of molecules that can successfully correlate with toxicity activity, achieving good accuracies on most data sets comparable to state-of-the-art models. We also test a physicochemical property, namely, the total energy of a molecule, to inform the toxicity prediction with a physical prior. However, our work suggests that these two properties can not be related. We also provide an attention weight analysis for helping to understand the toxicity prediction in 3D space and thus increase the explainability of the ML model. In summary, our findings offer promising insights considering 3D geometry information via EGNNs and provide a straightforward way to integrate molecular conformers into ML-based pipelines for predicting and investigating toxicity prediction in physical space. We expect that in the future, especially for larger, more diverse data sets, EGNNs will be an essential tool in this domain.</p> <p>PAPER</p> <p>https://pubs.acs.org/doi/full/10.1021/acs.chemrestox.3c00032</p> <p>CODE and MODELS:</p> <p>The conformer data sets and trained toxicity models will be published upon acceptance of this work. The code has been made available at <a href="https://github.com/jule-c/ET-Tox">https://github.com/jule-c/ET-Tox</a>, and the processed data as well as pretrained models for training and testing can be downloaded from <a href="../record/7942946">https://zenodo.org/record/7942946</a>. We can provide the full list of conformers as XYZ files upon request.</p>
Supplementary data for global distribution of mercury in foliage predicted by machine learning
<p>Global distribution of foliar mercury concentrations and pools with a spatial resolution of 0.25 latitude by 0.25 longitude, predicted by machine learning.</p>
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
<p>Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (<em>Pinus pinaster</em> Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.</p>
Predicting metal-protein interactions using cofolding methods: Status quo
<p>Metals play important roles for enzyme function and many therapeutically relevant proteins. Despite the fact that the first drugs developed via computer aided drug design were metalloprotein inhibitors, many computational pipelines still discard metalloproteins due to the difficulties of modelling them computationally. New "cofolding" methods such as AlphaFold3 (AF3) and RoseTTAfold-AllAtom (RFAA) promise to improve this issue by being able to dock small molecules in presence of multiple complex cofactors including metals or covalent modifications. Here, we analyze the current status for metal ion prediction using these methods. We find that currently only AF3 provides realistic predictions for metal ions, RFAA in contrast does perform worse than more specialized models such as AllMetal3D in predicting the location of metal ions accurately. We find that AF3 predictions are consistent with expected physico-chemical trends/intuition whereas RFAA often also predicts unrealistic metal ion locations.</p>
Data from: Semi-natural habitat, but not aphid amount or continuity, predicts lady beetle abundance across agricultural landscapes
<p>The amount of semi-natural habitat surrounding farm fields is a common but inconsistent predictor of natural enemy populations and predation services. Standard land cover metrics may not accurately capture the actual availability of limiting resources for natural enemies and can miss important dynamics across space and time. Theory from animal movement and landscape ecology predicts that regions with more, spatio-temporally continuous resources (i.e. food, shelter) should have larger predator populations and enhanced biological control. To test these predictions empirically, we designed a study measuring aphids, lady beetles, and predation services in agricultural landscapes in Wisconsin, USA. In two study years, we sampled lady beetles and aphids in 336 crop fields (corn, soybean, alfalfa, and small grains) and adjacent semi-natural habitat patches (grasslands and woodlands) across 24 1.5 km buffer landscapes at 4–7 time points each, and in one year we assessed predation rates with sentinel egg cards. We used aphid counts to model habitat-specific aphid phenologies, from which we calculated landscape indices of prey amount and continuity. These indices, along with semi-natural habitat area, were used to predict lady beetle abundance. While there were strong differences in the abundance and timing of aphids by habitat, semi-natural habitat amount was still a better predictor of lady beetle counts and sentinel egg predation than either aphid amount or continuity indices in these landscapes.</p> <p>Synthesis and application: Our findings confirm the robust relationship between lady beetles and semi-natural habitat in agricultural landscapes, and highlight the complexities of measuring fine-scale resource heterogeneity in real landscapes. Retaining or adding woodland and grassland patches in agricultural landscapes is likely to support larger lady beetle populations and enhance predation in crop fields. Our results suggest that these habitats may be more important for shelter than prey continuity, though this mechanism warrants further investigation. Future work should continue to refine experimental methods for the successful integration of landscape ecology and animal behavior to support conservation goals.</p>
NASA GES-DISC Knowledge Graph for Link Prediction
<p>This dataset includes a knowledge graph of NASA GES-DISC collections, featuring interconnected nodes for datasets, data center, projects, platforms, instruments, science keywords, and publications. Designed for link prediction tasks, it aids machine learning research in satellite observation, remote sensing, and climate change. The dataset is in CSV format, ready for graph databases and ML frameworks.</p> <p> </p>
INSPIRED: Inelastic Neutron Scattering Prediction for Instantaneous Results and Experimental Design
<p>INSPIRED is a graphic user interface (GUI) that performs rapid prediction and calculation of phonons and inelastic neutron scattering (INS) spectra. It consists of three modules. The "Predictor" module uses a symmetry-aware neural network (coupled with an autoencoder) [1-3] to perform direct prediction of total/partial phonon density of states and powder 1D/2D INS spectra from a given structure. The "DFT database" module uses pre-calculated force constants from density functional theory (DFT) [4] to perform INS simulations for single crystals and powders (for the crystals available in the database). The "MLFF" module uses pre-trained universal force fields [8-12] to perform structural optimization, phonon calculation, and INS simulations for single crystals and powders for any given crystal. The predicted/calculated results are saved in CSV files and can be visualized with the GUI. INSPIRED is developed to be a convenient tool for INS experimental planning, steering, and quick data analysis.</p> <p>This repository contains two files as an update to the previous version:</p> <p>1. A tarball file (dftdb.tar.gz) containing the DFT database (currently with 12734 crystals)</p> <p>2. A VirtualBox appliance file (inspired_vm.ova) to run INSPIRED as a virtual machine.</p> <p>The ML model file (model.tar.gz) remains the same and can be obtained from the previous version.</p> <p>Instructions on how to use these files, as well as the rest part of the software, can be found on the <a href="https://github.com/cyqjh/inspired">GitHub page</a>. </p>
Predicting particle quality attributes of organic crystalline materials using Particle Informatics
<p>Dataset related to the publication: "Predicting particle quality attributes of organic crystalline materials using Particle Informatics" published in Powder Technology (<a title="Go to table of contents for this volume/issue" href="https://www.sciencedirect.com/journal/powder-technology/vol/443/suppl/C"><span>Volume 443</span></a>, 1 July 2024, 119927Volume 443, 1 July 2024, 119927). In this work, a novel quercetin solvate of dimethylformamide (QDMF) was studied. The crystal structure was solved using single crystal X-ray diffraction and analysed using synthon analysis and other particle informatics tools (<em>e.g.</em>, solvate analyser). The thermal behaviour and thermodynamic stability of QDMF were studied experimentally using Raman spectroscopy, ATR-FTIR spectroscopy, differential scanning calorimetry, and thermogravimetric analysis. A clear relationship between the two-step desolvation behaviour of QDMF and the type, strength, and directionality of the main bulk synthons characterizing the QDMF structure was observed. Additionally, the attachment energy model was used to predict the QDMF morphology, together with facet-specific topology and chemical nature of each of the dominant {001}, {110}, and {200} facets. The {200} facet was found to be significantly rougher than the other two; whereas, the {110} was characterized by a higher percentage of exposed DMF molecules compared to the other two facets. Specific scanning electron microscopy and contact angle measurements were used to experimentally detect differences among the three facets and validate the modelling results.</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.