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13,064 results for “predictions”
Data: Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts
<p>Data for: <br><br>Soil moisture modeling with ERA5-Land retrievals, topographic indices, and in situ measurements and its use for predicting ruts</p> <p>Marian Schönauer<sup>1</sup>, Anneli M. Ågren<sup>2</sup>, Klaus Katzensteiner<sup>3</sup>, Florian Hartsch<sup>1</sup>, Paul Arp<sup>4</sup>, Simon Drollinger<sup>5</sup>, Dirk Jaeger<sup>1</sup></p> <p><sup>1</sup>Department of Forest Work Science and Engineering, University of Göttingen, Göttingen, Germany</p> <p><sup>2</sup>Department of Forest Ecology and Management, Swedish University of Agricultural Sciences, Umeå, Sweden</p> <p><sup>3</sup>Institute of Forest Ecology, University of Natural Resources and Life Sciences, Vienna, Vienna, Austria</p> <p><sup>4</sup>Forestry and Environmental Management, University of New Brunswick, New Brunswick, Canada</p> <p><sup>5</sup>Department of Physical Geography, University of Göttingen, Göttingen, Germany</p>
RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing
<p>This dataset accompanies the study "RepliChrom: Interpretable machine learning predicts cancer-associated enhancer-promoter interactions using DNA replication timing". The study introduces RepliChrom, a computational framework designed to predict enhancer–promoter interactions (EPIs) by leveraging multi-scale replication timing (RT) signals. This approach addresses the fundamental challenge of distinguishing gene targets regulated by distal enhancers from those activated by proximal transcriptional activity-a key problem in understanding the causal basis of complex diseases.</p> <p>Despite recent advances in high-throughput technologies such as Hi-C, ChIA-PET, and Hi-TrAC that allow genome-wide reconstruction of 3D chromatin architecture, the role of DNA replication timing in mediating these spatial interactions remains underexplored. RepliChrom fills this gap by using cell-type-specific RT profiles as predictive features for chromatin interaction inference.</p> <p>To support model development, training, and evaluation, we provide a comprehensive multi-omics dataset covering six human cell lines (K562, GM12878, HeLaS3, IMR90, NHEK, and HUVEC ), encompassing:</p> <p>Hi-C datasets: Processed chromatin interaction loops used to define positive and negative enhancer–promoter interaction pairs.</p> <p>ChIA-PET datasets: Interaction data anchored around transcription factor binding, including POLR2A and CTCF, used for model validation across different interaction types.</p> <p>Hi-TrAC datasets: Targeted chromatin accessibility-derived interaction data, offering complementary validation of the model on alternate platforms.</p> <p>Replication Timing (RT) data: Processed RT signal profiles for each cell line, used to extract multi-scale temporal features as inputs for RepliChrom.</p> <p>These datasets enable reproducibility of the model training process and serve as benchmark resources for future research into DNA replication–mediated regulation of 3D genome architecture.</p> <p><strong>Included Files</strong></p> <p>Hi-C_datasets.zip (2.23 MB): Processed Hi-C interaction pairs for six cell types.</p> <p>ChIA-PET_datasets.zip (818.18 KB): CTCF and POLR2A ChIA-PET interactions across multiple lines.</p> <p>Hi-TrAC_datasets.zip (196 bytes): Hi-TrAC-based chromatin interaction training datasets across multiple lines.</p> <p>Cellline_RT_data.zip (86.17 MB): Replication timing signal data across multiple human cell types for multi-scale replication timing feature extraction.</p> <p><strong>Usage</strong><br>All datasets are intended for academic, non-commercial use. The provided files can be directly used to reproduce the training and evaluation of RepliChrom, and may also support broader applications in enhancer–promoter modeling, replication-timing analysis, and 3D genomics studies. For detailed usage instructions and code implementation, please refer to the GitHub repository: https://github.com/DaoFuying/RepliChrom</p>
Data set for study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"
<p>Input data for the study "Thermal Dynamic Models for Predicting the Indoor Temperature of Multi-Zone Buildings"</p>
Habitat structural complexity predicts cognitive performance and behavior in western mosquitofish
<p>Urbanization and stream order alter freshwater habitat complexity (defined as the degree of variation in physical habitat structure). More complex habitats have more variation in habitat structure. Habitat complexity affects species composition and shapes animal ecology, behavior, and cognition. We used a delayed detour test to measure whether motor self-regulation and behavior of Western mosquitofish, <em>Gambusia affinis, </em>varied with habitat structural complexity that was quantified for nine populations. We predicted that motor self-regulation, motivation, and risk-taking behavior would increase with increasing habitat complexity, yet we found the opposite relationship. Lower complexity habitats offer less refuge which could increase predation pressure and select for greater risk-taking by fish with greater motor self-regulation. Our findings provide insight into how habitat complexity is related to cognitive processes and behavioral outcomes, and provide an explanation for why some species have a higher tolerance for urbanized environments.</p>
GTN: Predicting EI+ mass spectra with QCxMS
<p>Dataset for the Galaxy Training for mass spectra prediction with QCxMS. The file contains the structures for Ethylene, Benzophenone, Enilconazole and Mirex.</p>
Data from: Phenotypically plastic responses to environmental variation are more complex than life history theory predicts
<p>For insects that exhibit wing polyphenic development, abiotic and biotic signals dictate the adult wing morphology of the insect in an adaptive manner such that in stressful environments the formation of a flight-capable morph is favored and in low stress environments a flightless morph is favored. While there is a relatively large amount known about the environmental cues that dictate morph formation in wing polyphenic hemipterans like planthoppers and aphids, whether those cues dictate the same morphs in non-hemipteran (i.e. cricket) wing polyphenic species has not been explicitly investigated. To experimentally test the generality of environmental cue determination of wing polyphenism across taxa with diverse life histories, in this study we tested the importance of food quantity, parasitic infection, and tactile cues on wing morph determination in the wing polyphenic sand field cricket, <em>Gryllus firmus</em>. Our results also show that certain stress cues, such as severe diet quantity limitation and parasitic infection, actually led to an increase in the production of flightless morph. Based on these findings, our results suggest that physiological and genetic constraints are important to an organism's ability to respond to environmental variation in an adaptive manner beyond simple life history trade-offs.</p>
Figure 3 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 3. Location map of the study area.
ESM-2 embeddings for TCR-Epitope Binding Affinity Prediction Task
<p>This is the accompanying dataset that was generated by the GitHub project: <a href="https://github.com/tonyreina/tdc-tcr-epitope-antibody-binding">https://github.com/tonyreina/tdc-tcr-epitope-antibody-binding</a>. In that repository I show how to create a machine learning models for predicting if a T-cell receptor (TCR) and protein epitope will bind to each other.</p> <p>A model that can predict how well a TCR bindings to an epitope can lead to more effective treatments that use immunotherapy. For example, in anti-cancer therapies it is important for the T-cell receptor to bind to the protein marker in the cancer cell so that the T-cell (actually the T-cell's friends in the immune system) can kill the cancer cell.</p> <div> <div>[HuggingFace](https://huggingface.co/facebook/esm2_t36_3B_UR50D) provides a "one-stop shop" to train and deploy AI models. In this case, we use Facebook's open-source [Evolutionary Scale Model (ESM-2)](https://github.com/facebookresearch/esm). These embeddings turn the protein sequences into a vector of numbers that the computer can use in a mathematical model.</div> <div> </div> To load them into Python use the Pandas library:</div> <pre><code>import pandas as pd train_data = pd.read_pickle("train_data.pkl") validation_data = pd.read_pickle("validation_data.pkl") test_data = pd.read_pickle("test_data.pkl")</code></pre> <p>The <strong>epitope_aa</strong> and the <strong>tcr_full</strong> columns are the protein (peptide) sequences for the epitope and the T-cell receptor, respectively. The letters correspond to the <a href="https://en.wikipedia.org/wiki/DNA_and_RNA_codon_tables">standard amino acid codes</a>.</p> <p>The <strong>epitope_smi</strong> column is the <a href="https://en.wikipedia.org/wiki/Simplified_molecular-input_line-entry_system">SMILES</a> notation for the chemical structure of the epitope. We won't use this information. Instead, the ESM-1b embedder should be sufficient for the input to our binary classification model.</p> <p>The <strong>tcr</strong> column is the CDR3 hyperloop. It's the part of the TCR that actually binds (assuming it binds) to the epitope.</p> <p>The <strong>label</strong> column is whether the two proteins bind. 0 = No. 1 = Yes.</p> <p>The <strong>tcr_vector</strong> and <strong>epitope_vector</strong> columns are the bio-embeddings of the TCR and epitope sequences generated by the Facebook ESM-1b model. These two vectors can be used to create a machine learning model that predicts whether the combination will produce a successful protein binding.</p> <p>From the TDC website:</p> <blockquote> <p>T-cells are an integral part of the adaptive immune system, whose survival, proliferation, activation and function are all governed by the interaction of their T-cell receptor (TCR) with immunogenic peptides (epitopes). A large repertoire of T-cell receptors with different specificity is needed to provide protection against a wide range of pathogens. This new task aims to predict the binding affinity given a pair of TCR sequence and epitope sequence.</p> <p>Weber et al.</p> </blockquote> <p>Dataset Description: The dataset is from Weber et al. who assemble a large and diverse data from the VDJ database and ImmuneCODE project. It uses human TCR-beta chain sequences. Since this dataset is highly imbalanced, the authors exclude epitopes with less than 15 associated TCR sequences and downsample to a limit of 400 TCRs per epitope. The dataset contains amino acid sequences either for the entire TCR or only for the hypervariable CDR3 loop. Epitopes are available as amino acid sequences. Since Weber et al. proposed to represent the peptides as SMILES strings (which reformulates the problem to protein-ligand binding prediction) the SMILES strings of the epitopes are also included. 50% negative samples were generated by shuffling the pairs, i.e. associating TCR sequences with epitopes they have not been shown to bind.</p> <blockquote> <p>Task Description: Binary classification. Given the epitope (a peptide, either represented as amino acid sequence or as SMILES) and a T-cell receptor (amino acid sequence, either of the full protein complex or only of the hypervariable CDR3 loop), predict whether the epitope binds to the TCR.</p> <p>Dataset Statistics: 47,182 TCR-Epitope pairs between 192 epitopes and 23,139 TCRs.</p> <p>References:</p> </blockquote> <ol> <li>Weber, Anna, Jannis Born, and María Rodriguez Martínez. “TITAN: T-cell receptor specificity prediction with bimodal attention networks.” Bioinformatics 37.Supplement_1 (2021): i237-i244.</li> <li>Bagaev, Dmitry V., et al. “VDJdb in 2019: database extension, new analysis infrastructure and a T-cell receptor motif compendium.” Nucleic Acids Research 48.D1 (2020): D1057-D1062.</li> <li>Dines, Jennifer N., et al. “The immunerace study: A prospective multicohort study of immune response action to covid-19 events with the immunecode™ open access database.” medRxiv (2020).</li> </ol> <blockquote> <p>Dataset License: CC BY 4.0.</p> <p>Contributed by: Anna Weber and Jannis Born.</p> </blockquote> <p> </p> <div>The Facebook ESM-2 model has the MIT license and was published in:</div> <div> </div> <div>* Zeming Lin et al, Evolutionary-scale prediction of atomic-level protein structure with a language model, Science (2023). DOI: 10.1126/science.ade2574 https://www.science.org/doi/10.1126/science.ade2574</div> <div> </div> <div>HuggingFace has several versions of the trained model.</div> <div> </div> <div> <table> <tbody> <tr> <td>Checkpoint name</td> <td>Number of layers</td> <td>Number of parameters</td> </tr> <tr> <td>esm2_t48_15B_UR50D</td> <td>48</td> <td>15B</td> </tr> <tr> <td>esm2_t36_3B_UR50D</td> <td>36</td> <td>3B</td> </tr> <tr> <td>esm2_t33_650M_UR50D</td> <td>33</td> <td>650M</td> </tr> <tr> <td>esm2_t30_150M_UR50D</td> <td>30</td> <td>150M</td> </tr> <tr> <td>esm2_t12_35M_UR50D</td> <td>12</td> <td>35M</td> </tr> <tr> <td>esm2_t6_8M_UR50D</td> <td>6</td> <td>8M</td> </tr> </tbody> </table> </div>
Figure 3 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 3. Flow chart of simulation procedure the study.
Figure 1 in Establishment of an expansion-predicting model for invasive alien cerambycid beetle Aromia bungii based on a virtual ecology approach
Figure 1. Study area and simulation unit
AlphaFold 3 predicted NLR resistosome structures
<p><strong>Abstract</strong></p> <p>In our paper "A disease resistance protein triggers oligomerization of its NLR helper into a hexameric resistosome to mediate innate immunity," we used the NbNRC2 hexamer structure to evaluate the capabilities of the newly introduced AlphaFold 3 in predicting activated CC-NLR oligomers. Our analysis highlights AlphaFold 3 effectiveness in confidently modelling the N-terminal alpha1-helices of NbNRC2 and other CC-NLRs, a structurally elusive region critical for NLR function but challenging to resolve through conventional structural methods. This study not only underscores the utility of AlphaFold 3 in enhancing our understanding of plant immune receptors but also extends its application to complex oligomerization processes in innate immunity. Here, we provide the Supplementary data accompanying the paper which includes metadata and predicted structures for 29 NLR immune receptors, providing valuable resources for further research in plant pathogen resistance.</p> <p> </p> <p><strong>Index:</strong></p> <ul> <li>Metadata:</li> <li> <ul> <li>Metadata, sequence, and model statistics of all modelled NLRs: <ul> <li>Data S1.xlsx</li> </ul> </li> </ul> </li> </ul> <p> </p> <ul> <li>Predicted structures:</li> <li> <ul> <li>AlphaFold 2 vs 3 vs 3+lipids comparison for NbNRC2: <ul> <li>AF_benchmark.zip</li> </ul> </li> </ul> </li> </ul> <ul> <li> <ul> <li>[AlphaFold 3] NRC2 oliogmoeric type benchmarks (N = 10): <ol> <li>NRC2_tetramers.zip</li> <li>NRC2_pentamers.zip</li> <li>NRC2_hexamers.zip</li> <li>NRC2_heptamers.zip</li> <li>NRC2_octamers.zip</li> </ol> </li> </ul> </li> </ul> <ul> <li> <ul> <li>[AlphaFold 3] Pentamer vs Hexamer comparison for 11 representative NRC proteins: <ul> <li>NRCs.zip</li> </ul> </li> <li>[AlphaFold 3] Pnetamer vs Hexamer comparison for AtZAR1 (PDB:6j5t) and TmSr35 (PDB:7xe0) experimentally validated cryo-EM structure: <ul> <li>Benchmarks.zip</li> </ul> </li> <li>[AlphaFold 3] Pentamer vs Hexamer comparison for a selection of 8 CC, 6 CCG10, and 2 CCR-NLRs: <ul> <li>CC.zip</li> <li>CCG10.zip</li> <li>CCR.zip</li> </ul> </li> </ul> </li> </ul>
Parcel level temporal variance of remotely sensed spectral reflectance predicts plant diversity
<p>Over the last two decades, considerable research has built on remote sensing of spectral diversity to assess plant diversity. The spectral variation hypothesis (SVH) proposes that spatial variation in reflectance data of an area is positively associated with plant diversity. While the SVH has exhibited validity in dense forests, it performs poorly in highly fragmented and temporally dynamic agricultural landscapes covered mainly by grasslands. Such underperformance can be attributed to the mosaic-like spatial structure of human-dominated landscapes with fields in varying phenological and management stages. Therefore, we argued for re-evaluating SVH's flawed window-based spatial analysis and underutilized temporal component. In particular, In particular, we captured the spatial and temporal variation in reflectance and assessed the relationships between spatial and temporal components of spectral diversity and plant diversity at the parcel level as a unit that relates to management patterns. Our investigation spanned three grasslands on two continents covering a wide spectrum of agricultural usage intensities. To calculate different components of spectral diversity, we used multi-temporal spaceborne Sentinel-2 data. We showed that plant diversity was negatively associated with the temporal component of spectral diversity across all sites. In contrast, the spatial component of spectral diversity was related to plant diversity in sites with larger parcels. Our findings highlighted that in agricultural landscapes, the temporal component of spectral diversity drives the spectral diversityplant diversity associations. Consequently, our results offer a novel perspective for remote sensing of plant diversity globally.</p>
Figure 2 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 2. Schematic view of Kernel Ridge Regression (KRR) model.
Figure 1 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 1. High-resolution flow chart of the Random Forest (RF) model.
Bark beetle predictive model results (FirEUrisk)
<p>This datasets contains three products developed within the FirEUrisk project. <strong>FirEUrisk</strong> is funded by the European Union as part of the Horizon 2020 framework program (Grant Agreement No. 101003890). It is focused on evaluating and promoting an integrated science-based strategy to improve forest fire risk assessments in Europe</p> <p><strong>FirEUrisk_BarkBeetleAttackPredic_50m_DE.CZ.PL_20240126_V01.tif</strong></p> <p>This product shows the probability of a bark beetle (<em>Ips typographus</em>) attack between September 2020 and October 2021. The assessment includes a set of risk factors that predispose forests to pest infestations and wildfires propagation for the FirEUrisk Central-Eastern Europe Pilot Site. This technological solution aims to identify areas prone to a bark beetle infestation using Earth Observation data and machine learning techniques. The geospatial results are provided in raster format (.tiff) to help managers optimise their resources and mitigate the negative effects of plagues. The results indicate the probability of a bark beetle attack, being ranged from 0 to 100.</p> <p> <br><strong>FirEUrisk_BarkBeetleDamage_10m_DE.CZ.PL_20240126_V01.tif</strong></p> <p>This product contains an estimate of the real bark beetle damage between September 2020 and October 2021. This estimate was derived from Sentinel-2 imagery and was used as target to train the predictive model.</p> <p> <br><strong>FirEUrisk_SpruceClassification_10m_DE.CZ.PL_20240126_V01.tif</strong></p> <p>This product contains a classification of spruce <em>(Picea abies) </em>area for September 2020. It was derived from Sentinel-2 imagery and used as input for the estimation of bark beetle damage and the predictive model.</p>
F-Type ATP synthase rotor ring stoichiometry predictions
<p>Stoichiometry prediction data for:</p> <p>1) rotor rings with experimental structures available</p> <p>2) representative c subunits</p> <p>3) c subunits with unexpectedly high predicted stoichiometry</p> <p>4) all c subunits via matching with corresponding representative sequence</p>
Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments
<p>This repo contains a complete replication package, including raw data and scripts for the statistical analysis, for the paper "Ghost Echoes Revealed: Benchmarking Maintainability Metrics and Machine Learning Predictions Against Human Assessments" submitted to the industry track of the <a href="https://conf.researchr.org/home/icsme-2024" rel="nofollow">40th International Conference on Software Maintenance and Evolution (ICSME)</a>, Flagstaff, AZ, USA, Oct 6-11, 2024.</p> <div> <h2>Authors</h2> <a href="https://github.com/codescene-research/maintainability-benchmarking-icsme-2024#authors"></a>Markus Borg, Marwa Ezzouhri, and Adam Tornhill</div> <div> <h2>Abstract</h2> <a href="https://github.com/codescene-research/maintainability-benchmarking-icsme-2024#abstract"></a>As generative AI is expected to increase global code volumes, the importance of maintainability from a human perspective will become even greater. Various methods have been developed to identify the most important maintainability issues, including aggregated metrics and advanced Machine Learning (ML) models. This study benchmarks several maintainability prediction approaches, including State-of-the-Art (SotA) ML, SonarQube's Maintainability Rating, CodeScene's Code Health, and Microsoft's Maintainability Index. Our results indicate that CodeScene matches the accuracy of SotA ML and outperforms the average human expert. Importantly, unlike SotA ML, CodeScene also provides end users with actionable code smell details to remedy identified issues. Finally, caution is advised with SonarQube due to its tendency to generate many false positives. Unfortunately, our findings call into question the validity of previous studies that solely relied on SonarQube output for establishing ground truth labels. To improve reliability in future maintainability and technical debt studies, we recommend employing more accurate metrics. Moreover, reevaluating previous findings with Code Health would mitigate this revealed validity threat.</div> <div> <h2>Repository Content</h2> Two Jupyter Notebooks.</div> <ul> <li> <ul> <li>uc1_maintainability_prediction.ipynb: A Notebook for Use Case 1 - Maintainability Prediction.</li> <li>uc2_liability_prediction.ipynb: A Notebook for Use Case 2 - Liability Prediction.</li> </ul> </li> <li>maintainability_data.csv: The dataset covering the 404 open-source files from the Maintainability Dataset (Bertrand <em>et al.</em>, 2020) <ul> <li>Majority vote ground truth labels from Schnappinger <em>et al.</em> (2020)</li> <li>Low-level code metrics from Bertrand <em>et al.</em> (2023)</li> <li>Code Health</li> <li>SonarQube output, i.e., TD Ratio and TD Time</li> <li>Microsoft Maintainability index provided by <a href="https://plugins.jetbrains.com/plugin/13959-metricstree" rel="nofollow">MetricsTree</a></li> </ul> </li> </ul> <div> <h2>References</h2> </div> <ul> <li>Schnappinger <em>et al.</em>, <a href="https://figshare.com/articles/dataset/A_Software_Maintainability_Dataset/12801215" rel="nofollow">A Software Maintainability Dataset</a>, 10.6084/m9.figshare.12801215, 2020.</li> <li>Schnappinger <em>et al.</em>, Defining a Software Maintainability Dataset: Collecting, Aggregating and Analysing Expert Evaluations of Software Maintainability, in <em>Proc. of the 36th International Conference on Software Maintenance and Evolution</em>, pp. 278–289, 2020.</li> <li>S. Bertrand <em>et al.</em>, Replication and Extension of Schnappinger’s Study on Human-level Ordinal Maintainability Prediction Based on Static Code Metrics, in <em>Proc. of the 27th International Conference on Evaluation and Assessment in Software Engineering</em>, pp. 241–246, 2023.</li> </ul>
Data for "Uncertainties too large to predict tipping times of major Earth system components from historical data"
<p>All data needed to reproduce the figures from the Science Advances manuscript "Uncertainties too large to predict tipping times of major Earth system components from historical data" by Ben-Yami et al.</p> <p>Figs12SamplePathData.zip and Fig3SamplePathData.zip includes the generated synthetic timeseries of the conceptual models in Figs 1-3.</p> <p>The .txt files are the different AMOC observational time series, with the file names structured as dataset_fingerprint.txt. C18 and C18_2GMT are the subpolar gyre SSTs minus one times and twice the global mean SSTs, respectively. The dipole fingerprint is as defined in the text. The first column is the date, and the second column the fingeprint values. The values are monthly means and thus the value for the day of the month in the date is not significant.</p>
A synthetic dataset for the exploration of survival and classification models: prediction of heart attack or stroke within a 10-year follow-up period
<div> <div></div> </div> <div> <div> <div> <p><span>Machine learning methodologies are increasingly popular in health care research. This shift to integrated data science approaches necessitates professional development of the existing health care data analyst workforce. To enhance a smooth transition, educational resources need to be developed. Barriers to accessing real healthcare datasets, vital for health care data analyses methodologies training purposes, include financial, ethical and patient confidentiality concerns. Synthetic datasets mimicking real-world complexities offer a simpler solution.</span></p> <p>We present a synthetic dataset which mirrors routinely collected primary care data on heart attack and stroke among the adult population. The data incorporates much of the practical challenges encountered in routinely collected primary care systems such as missing data, informative censoring, interactions, variable irrelevance, and noise and can be used for training in methods which handle these difficulties. The intent is for the user to build models of heart/stroke risk using survival-based methodologies.</p> <p>By sharing this synthetic dataset openly, our goal is to contribute a transformative asset for professional training in health and social care data analysis. The dataset covers demographics, lifestyle variables, comorbidities, systolic blood pressure, hypertension treatment, family history of cardiovascular diseases, respiratory functioning, and experience of heart-attack and/or stroke. This initiative aims to bridge the gap in sophisticated healthcare datasets for training, fostering professional development of the health and social care research workforce.</p> <p>This study is funded by the National Institute for Health and Care Research ARC Wessex and the National Centre for Research Methods. The views expressed in this summary are those of the author(s) and not necessarily those of the National Institute for Health and Care Research or the Department of Health and Social Care.</p> <p> </p> </div> </div> </div>
A quantitative approach to easily characterize and predict cell death and escape to β-lactam treatments
<p>This repository contains the raw experimental data corresponding to the paper entitled "A quantitative approach to easily characterize and predict cell death and escape to β-lactam treatments" and authored by Andreani, Gross and colleagues. </p> <p>The analysis of the data and the creation of the figures of the paper can be done using the code available at https://gitlab.inria.fr/InBio/Public/esbl-escape. A README file, briefly describing data organization, is provided.</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.